Method for monitoring growth of plants and generating a plant grow schedule

ABSTRACT

One variation of a method for monitoring growth of plants within a facility includes: aggregating global ambient data recorded by a suite of fixed sensors, arranged proximal a grow area within the facility, at a first frequency during a grow period; extracting interim outcomes of a set of plants, occupying a module in the grow area, from module-level images recorded by a mover at a second frequency less than the first frequency while interfacing with the module during the period of time; dispatching the mover to autonomously deliver the module to a transfer station; extracting interim outcomes of the set of plants from plant-level images recorded by the transfer station while sequentially transferring plants out of the module at the conclusion of the grow period; and deriving relationships between ambient conditions, interim outcomes, and final outcomes from a corpus of plant records associated with plants grown in the facility.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is a continuation of U.S. patent application Ser. No.15/955,651, filed on 17 Apr. 2018, which claims the benefit of U.S.Provisional Application No. 62/486,391, filed on 17 Apr. 2017, which isincorporated in its entirety by this reference.

This application is related to U.S. patent application Ser. No.15/852,749, filed on 22 Dec. 2017, and to U.S. patent application Ser.No. 15/872,299, filed on 16 Jan. 2018, both of which are incorporated intheir entireties by this reference.

TECHNICAL FIELD

This invention relates generally to the field of agricultural implementsand more specifically to a new and useful method for monitoring growthof plants and generating a plant grow schedule in the field ofagricultural implements.

BRIEF DESCRIPTION OF THE FIGURES

FIG. 1 is a flowchart representation of a method;

FIG. 2 is a flowchart representation of one variation of the method;

FIG. 3 is a flowchart representation of one variation of the method;

FIG. 4 is a flowchart representation of one variation of the method; and

FIG. 5 is a flowchart representation of one variation of the method.

DESCRIPTION OF THE EMBODIMENTS

The following description of embodiments of the invention is notintended to limit the invention to these embodiments but rather toenable a person skilled in the art to make and use this invention.Variations, configurations, implementations, example implementations,and examples described herein are optional and are not exclusive to thevariations, configurations, implementations, example implementations,and examples they describe. The invention described herein can includeany and all permutations of these variations, configurations,implementations, example implementations, and examples.

1. Method

As shown in FIG. 1, a method S100 for monitoring growth of plantsincludes: collecting a first set of images and a first set of ambientdata over a group of modules through a set of fixed sensors at a firstrate and at a first resolution in Block S110, each module in the groupof modules housing multiple plants; writing data from the first set ofimages and the first set of ambient data to plant records assigned toplants occupying the group of modules in Block S112; navigating a moverto a first module in the group of modules, the mover including a set ofmobile sensors in Block S120; collecting a second set of images and asecond set of ambient data over the first module through the set ofmobile sensors at a second rate and at a second resolution in BlockS130, the second rate less than the first rate, the second resolutiongreater than the first resolution; writing data from the second set ofimages and the second set of ambient data to plant records assigned toplants occupying the first module in Block S132; delivering the firstmodule to a transfer station in Block S140; while transferring plantsfrom the first module, collecting a third set of images at a third rateand at a third resolution in Block S150, each image in the third set ofimages representing a plant occupying the first module, the third rateless than the second rate, the third resolution greater than the secondresolution; writing data from the third set of images to plant recordsassigned to plants occupying the first module in Block S152; determiningoutcomes of plants in the first module based on the third set of imagesin Block S160; calculating a set of relationships between ambient dataand outcomes stored in plant records in Block S170; and generating agrow schedule for new plants loaded into the group of modules based onthe set of relationships between ambient data and outcomes in BlockS172.

One variation of the method includes: accessing a series of globalambient data recorded by a suite of fixed sensors, arranged proximal agrow area within the facility, at a first frequency during a firstperiod of time in Block Silo; writing global ambient data to plantrecords associated with plants occupying a group of modules within thegrow area based on known locations of modules within the grow area andknown locations of plants within the group of modules during the firstperiod of time in Block S112; dispatching a mover to autonomouslynavigate to the group of modules during the first period of time inBlock S120; accessing a series of module-level images of a first module,in the group of modules, recorded by the mover at a second frequencyless than the first frequency during the first period of time in BlockS130; extracting interim outcomes of a first set of plants, occupyingthe first module, from the series of module-level images and writinginterim outcomes of plants in the first set of plants to plant recordsassociated with plants in the first set of plants based on knownlocations of the first set of plants within the first module during thefirst period of time in Block S132; dispatching the mover toautonomously deliver the first module to a transfer station in BlockS140; for each plant in the first set of plants, accessing a plant-levelimage of the plant recorded by the transfer station while transferringthe plant out of the first module during a second period of timesucceeding the first period of time in Block S150, extracting a finaloutcome of the plant from the plant-level image, and writing the finaloutcome to a plant record associated with the plant in Block S152; andderiving relationships between ambient conditions, interim outcomes, andfinal outcomes from a corpus of plant records associated with plantsgrown in the facility in Block S160.

2. Applications

Generally, the method S100 can be executed by a computer system inconjunction with a greenhouse or other agricultural facility(hereinafter the “facility”): to autonomously collect optical data ofplants housed and transferred between growing modules (hereinafter“modules”) through various fixed and mobile high-, moderate-, andlow-resolution optical sensors within the facility; to autonomouslycollect ambient data from near these plants over time through acombination of fixed and mobile high- and low-resolution ambientsensors; to collect plant outcome data, such as size, weight, visualappeal, flavor profile (or “taste”), and nutritional composition, forall or select plants when harvested; to amass these optical, ambient,and outcome data in plant-specific data containers (e.g., discrete filesor vectors); and to develop a model for predicting outcome of a singularplant at harvest (and during growth cycles) based on ambient conditionsaround the plant and/or qualities of the plant recorded days, weeks, ormonths before the plant is harvested.

2.1 Applications: Data Collection from Fixed and Mobile Infrastructure

In particular, the computer system can interface with fixed sensorsarranged in the facility—such as a fixed camera arranged overhead a growarea and a small number of fixed temperature, humidity, light level(e.g., UV, infrared, near-infrared, or visible light level), and windspeed sensors arranged in select locations throughout the grow area—tocollect relatively low-resolution optical data (e.g., color photographicimages) of a group of modules in the grow area and ambient conditionsnearby at relatively high frequency (e.g., once per minute), therebyenabling the computer system to monitor high-level visual and ambientconditions of plants in these modules in near-real-time. The computersystem can also interface with an autonomous mover that is configured toautonomously navigate to modules throughout the grow area and to recordmodule-specific data of singular adjacent modules through a suite ofintegrated mobile sensors—such as a mobile camera and mobiletemperature, humidity, light level, wind speed, water temperature, pH,and dissolved oxygen sensors; the computer system can collectmoderate-resolution optical and ambient data for all plants in onemodule at moderate frequency (e.g., once per day), thereby enabling thecomputer system to intermittently monitor visual and ambient conditionsof multiple plants in a singular module (e.g., daily). Furthermore, thecomputer system can interface with a robotic manipulator and/or anoptical inspection station at a transfer station at a fixed locationwithin the facility to collect weight data and high-resolution opticaldata of a singular plant extracted from a module when the mover deliversthis module to the transfer station, such as once per two-week interval.The computer system can also selectively flag a subset of plants (e.g.,one plant per ten modules) delivered to the transfer station at harvesttime for further testing, such as composition or taste testing at anexternal facility or manual labeling of the plant's visual appeal by ahuman grower. The computer system can thus interface with fixedinfrastructure and mobile infrastructure to collect low-resolution dataat high frequency, moderate-resolution data at moderate frequency,high-resolution data at low frequency, and select plant outcome data ona limited basis.

The computer system can then: quickly detect high-level signs ofcomplications in a subset of modules in the grow area (e.g., wilting,chemical burns) from these low-resolution data; regularly (e.g., daily)detect lower-level signs of complications (e.g., signs of pests,chemical burns) and general plant characteristics within a singularmodule from these moderate-resolution data; and intermittently detectplant-specific complications and plant-specific characteristics fromthese high-resolution data. The system can also bolster plant outcomedata with selective testing of individual plants.

The computer system can therefore monitor plants across groups ofmodules, sets of plants in singular modules, and singular plants growingwithin a facility—containing thousands or millions of plants in variousstages of growth at any instant in time—based on data collected bylimited fixed and mobile infrastructure deployed within the facility,thereby balancing reaction times to complications, value of individualplants at various stages of growth, cost of infrastructure, and cost ofthird-party plant testing.

2.2 Applications: Grow Schedule

The computer system can then compile these input and output data into amodel that links certain inputs—such as air temperature, humidity, lightlevel, variance of dissolved oxygen, leaf area, leaf color, plantdiameter, plant height, etc. over intervals of minutes, hours, days, orweeks—during growth of a plant to the expected outcome—such as visualappeal, size, and taste—of the plant at harvest. The computer system canimplement this model to automatically define or adjust growth inputparameters realized autonomously by systems throughout the facilitywhile growing new plants in order to drive these new plants towardhigher quality, greater visual appeal, greater yield, reduced cost,reduced pest pressures (e.g., due to improved pest detection andmitigation procedures), improved resistance to pests, etc. The systemcan then autonomously implement these adjusted growth input parameters(e.g., in a revised “grow schedule”) for new plants grown at thefacility. The system can then repeat these processes over time tocollect plant maturation data for these new plants, to collect plantoutcome data for these new plants, to refine the model, and to revisegrowth input parameters for a next batch of plants grown at thefacility.

The system can therefore: aggregate input data (e.g., growth inputparameters, target ambient conditions) and output data (e.g., size,shape, color, visual appeal, and/or taste at harvest) across a group ofplants (e.g., thousands or millions of plants); implement machinelearning, artificial intelligence, and/or other statistical techniques(e.g., regression analysis, least square error analysis) to derivecorrelations between input parameters (e.g., air flow, air temperature,relative humidity, water nutrient level, light level, dissolved oxygen,etc.) and outcomes for these plants; represent these correlations in amodel; define target values and tolerance ranges for various inputparameters based on correlations with various outcomes represented inthis model; and generate a temporal grow schedule that, when implementedfor new plants grown in the facility, is predicted to yield certaintarget plant outcomes (e.g., a combination of target plant size, color,nutrient composition, and flavor profile, etc.).

2.3 Applications: Single Plant Outcome Prediction

Furthermore, the system can track development of an individual plant inthe facility over time based on features extracted from low-, moderate-,and high-resolution images of this plants over time and predict a futureoutcome of this plant accordingly. For example, the system can: compareimages of a new plant to images of previous plants of known outcomes;visually match the new plant to a single previous plant or to acomposite representation (e.g., an average) of multiple previous plantsof similar outcomes (hereinafter a “previous plant”), and predict theoutcome of the new plant based on the outcome of the previous plant thatexhibits greatest similarity (or “proximity”) to the new plant. Thesystem can then: maintain a current grow schedule for the plant (andother plants in the same module) if the known outcome is positive; cullthe plant (e.g., upon next delivery by the mover to the transferstation) if the known outcome is negative; or identify a second previousplant associated with a positive outcome but exhibiting visualsimilarities with the new plant at approximately the same stage ofdevelopment and shift the grow schedule of the new plant toward the growschedule realized for the second plant in order to improve likelihood ofa positive outcome for the new plant.

2.4 Infrastructure and Plant Flow

As shown in FIGS. 1, 4, and 5, the method S100 can be executed by asystem, such as including: a local or remote computer system (e.g., aremote server); a robotic manipulator connected to the computer system,located at a transfer station within the facility, and outfitted with acamera, scale, and/or end effector configured to retrieve plants frommodules; and/or an autonomous “mover” configured to autonomouslynavigate throughout the facility, to record data (e.g., optical,ambient, and/or contact-based data) of individual modules, to delivermodules to the transfer station responsive to commands issued by thecomputer system, and to return modules to their assigned locationsthroughout the facility.

In particular, plants may be grown in modules containing a growing trayand defining an array of plant slots configured to hold one plant (or a“bunch” of like plants, such as multiple basil plants). Young plants (or“seedlings”) may have relatively small leaves covering a relativelysmall area such that these young plants require only a small growvolume; as these plants mature (e.g., to a “sapling” stage or through“thinning” and “rosette” stages”), their leaves may grow to cover agreater area, thereby requiring a larger grow volume; as these plantsmature further (e.g., through “early-heading,” “mid-heading” and“mature-heading” stages”), their leaves may develop more fully to covera greater area up to harvest, thereby necessitating an even larger growvolume. In order to maintain a relatively high throughput per floor areawithin the facility, the facility can be outfitted with modules ofdifferent types—that is, modules with different plant slot densitiessuited to various stages of plant growth and therefore to various sizeranges of plants from seeding to harvest. For example, the facility canbe outfitted with: seeding trays (or “seeding modules”) defining ahighest density of plant slots (e.g., 640 plant slots per 4-foot by12-foot module) and configured to hold plants during a seedling stage;modules of a first type (hereafter a “nursery type”) defining a moderatedensity of plant slots (e.g., plant slots per 4-foot by 12-foot module)and configured to hold plants during a sapling stage; and modules of asecond type (hereinafter a “finishing type”) defining a lowest densityof plant slots (e.g., 40 plant slots per 4-foot by 12-foot module) andconfigured to hold plants during a finishing stage and up to harvest. Byplacing young plants first in modules with greatest plant slot densitiesand then transiting these plants to modules characterized by lower andlower plant slot densities as the plants increase in size and maturity,the facility can house and grow more plants per module on average andtherefore achieve greater space efficiency (i.e., a number of plants perfloor area within the facility).

As modules—and plants occupying these modules—are stationed andrepositioned throughout the facility over time, various fixed and mobilesensors within the facility can collect general, module-specific, andplant specific ambient data, water quality data, and optical data. Thesystem can then: merge these data to derive correlations between ambientconditions, water qualities, and visual characteristics of plants andtheir eventual outcomes; and develop grow schedules for future plantsgrown in the facility in order to achieve certain outcomes.

3. System

The method S100 can be executed by a system including: a computersystem; a fixed sensor suite; an automated (or “autonomous”) mover; anda robotic manipulator. The fixed sensor suite is connected to thecomputer system and is configured to regularly collect optical data(e.g., overhead digital photographic images) of multiple modules—eachcontaining multiple plants—and to collect ambient sensor data from overthese modules, such as once per hour or once per second. The mover isconnected to the computer system and configured: to navigate to singlemodules throughout the facility; to collect optical data from groups ofplants in a single module and to collect water quality data from thesemodules, such as once per day or every other day; and to deliver singlemodules to a transfer station and to return modules to their assignedlocations throughout the facility, such as once per two-week intervalper module. The robotic manipulator is connected to the computer system,is located at a transfer station within the facility, and is configuredto collect optical, weight, and/or other data from individual plantswhile moving plants between modules, such as from a nursery module to afinishing module.

In particular, the mover can be configured to automatically navigatethroughout the facility to a particular location under or near a module,to couple to or lift the module, to navigate—with the module—to atransfer station within the facility, and to release (or “deposit”) themodule at the transfer station. While moving past other modules on itsway to collecting a particular module for delivery to the transferstation, the mover can also collect optical and water quality data fromthese other modules. The robotic manipulator can be arranged near thecenter of the transfer station, and the mover can arrange a first moduleof a nursery type (e.g., containing a high density of plant slots) and asecond module of a finishing type (e.g., containing a lower density ofplant slots) adjacent the robotic manipulator at the transfer station inorder to enable the robotic manipulator to navigate its end effectoracross both the full extent of plant slots in the first module and thefull extent of plant slots in the second module. The mover can alsodeposit a third module of the finishing type to the transfer station,such as adjacent the second module, and the robotic manipulator cantransition to transferring cleared plants from the first module to thethird module once all plant slots in the second module are filled. Themover can then return the second and third modules to assigned growareas within the facility, such as under a translucent roof and/or underartificial lighting.

The mover can also deliver a seeding tray to the transfer module, andthe robotic manipulator can implement similar methods and techniques tocheck sizes and weights of plants in the seeding tray and tosequentially transfer plants from the seeding tray into the first modulebefore the mover returns the first module to an assigned grow areawithin the facility. Alternatively, the system can include a secondrobotic manipulator arranged at a second transfer station within thefacility, and the mover can deliver the first module and the seedingtray to the second transfer station, and the second robotic manipulatorcan transfer seedlings from the seeding tray into the first module.

Similarly, the (first) robotic manipulator at the (first) transferstation, the second robotic manipulator at the second transfer station,or a third robotic manipulator at a third transfer station within thefacility can remove plants from the second and third modules of thefinishing type (e.g., for manual or automated processing, such asremoval of roots) and/or place plants from the second and third modulesinto packages (e.g., boxes, pallets) for distribution from the facility.

The method S100 is described below as executed by the system: to collectlow-resolution plant related-data at a high frequency through fixedinfrastructure (i.e., a fixed sensor suite) within the facility; tocollect moderate-resolution plant related-data at a moderate frequencythrough mobile infrastructure (i.e., a mobile sensor suite on themover); to collect high-resolution plant related-data at a low frequencythrough a single plant manipulator (e.g., the robotic manipulator);while also autonomously moving plants from modules containing highdensities of plant slots to modules containing lower densities of plantslots to accommodate growth of these plants over time. The computersystem executing Blocks of the method S100 can then: selectively writethese optical and numerical-point data to plant records assigned to eachindividual plant; collect final results of these plants, such asnutrient values, flavor profile, and visual appeal; and extrapolatecombinations and ranges of inputs that yield maximum, target, orsufficient compromises of nutrient values, flavor profile, grow time,and cost, etc.

The method S100 is also described as executed by the system toautomatically transfer lettuce through a sequence of seeding trays,nursery-type module, and finishing-type modules. However, the methodS100 can be implemented in a greenhouse or other facility in conjunctionwith growing any other type of plant, such as fruit, vegetables,legumes, flowers, shrubs, or trees, etc.

4. Modules

The system includes a set of modules configured to house a group ofplants throughout a segment of the growth cycle of these plants (e.g.,four weeks of a twelve-week grow-period). Each module can define astandard size (e.g., four feet in width by eight feet in length by fourfeet in height; two meters in width by five meters in length by onemeter in height) and can include a number of plant slots matched to thesegment of plant growth cycle associated with the module. For example: aseeding-type module can include 192 plant slots; a nursing-type modulecan include 48 plant slots (i.e., one-quarter as many as seeding-typemodules); and a finishing-type module can include twelve plant slots(i.e., one-quarter as many as nursing-type modules); as shown in FIG. 5,despite these modules defining the same overall size and geometry.

4.1 Hydroponic Trays

In one implementation, a module includes: a set of hydroponic trays (orhydroponic tubes), each defining a (linear) array of plant slots,wherein each plant slot is configured to receive and retain one plant(or one cluster of multiple plants); a carriage or frame supporting theset of hydroponic trays at an angle, such as declining 5° fromhorizontal; a reservoir fluidly coupled to the set of hydroponic traysand configured to collect water flowing out of the hydroponic trays; anda pump configured to cycle water from the reservoir back through the setof hydroponic trays. The module can additionally or alternatively beconfigured to transiently connect to a water supply line and to a waterreturn line in the facility, which can provide a constant supply ofwater and nutrients to plants in this module. In this implementation,the module can also include: one optical fiducial at the front of eachhydroponic tray; optical fiducials at the end of each hydroponic tray;one optical fiducial adjacent each plant slot along each hydroponictray; or optical fiducials at three or four corners of the modules; etc.The system can thus detect these optical fiducials—such as throughoptical sensors integrated into the mover and into the roboticmanipulator—to identify and locate the module and to locate plant slotsin each hydroponic tray in the module.

4.2 Open Trays

In another implementation shown in FIGS. 4 and 5, a module includes: anopen tray configured to contain a standing volume of water andnutrients; a cover arranged over the open tray and including a set ofperforations, wherein each perforation defines a plant slot configuredto receive and retain one plant (or one cluster of plants); and a standconfigured to support the tray off of the ground. In the implementation:the open tray can define a standard rectangular geometry, as describedabove; and the lid can include a rectangular cover configured to floatin water in the tray. For example, the lid can include: a rigid panel(e.g., nylon or aluminum sheet) defining an array (e.g., a linear gridarray, a close-pack array) of plant slots; and floats extending acrossthe underside of the rigid panel and exhibiting sufficient buoyancyand/or height to maintain an air gap between the top surface of water inthe tray and the bottom surface of the lid when the array of plant slotsin the lid are filled with plants, thereby maintaining exposure toair—and therefore oxygen—for upper root systems of these plants.Furthermore, in this example, because the lid floats on the water in thetray, the lid can ensure that roots of these plants remain in contactwith water in the tray despite changes to the water level in the tray.

Furthermore, in this implementation, the module can include a set ofoptical fiducials arranged on the top surface of the lid and/or the trayand configured to indicate position, orientation, distance, type, and/orunique identity of the module. For example, the module can include: oneoptical fiducial (e.g., a unique barcode or quick-response code)arranged at each of three or four corners on the lid; three (identical)colored dots (e.g., yellow for nursery stage, red for finishing stage)arranged at corners of the lid or tray; or one optical fiducial adjacenteach plant slot on the lid (e.g., a colored circle, square, or polygonof known geometry and dimension encircling each plant slot); etc.

Alternatively, the module can include an open tray with a fixed lid. Inthis implementation, the tray and fixed lid can define geometries andfeatures similar to those in the foregoing implementation but with thelid fixedly coupled to the rim of the tray, such as sealed against therim of the tray to prevent water from splashing out of the tray when themodule is moved by the mover.

However, a module can define any other structure or geometry and candefine any other number or arrangement of plant slots.

5. Fixed Infrastructure

As shown in FIG. 1, the system can include a fixed optical sensor (e.g.,a color camera) arranged in a fixed location over modules in thefacility and configured to regularly record images of multiple plantsacross multiple modules below. For example, the fixed optical sensor canbe mounted to the ceiling of the facility or coupled to artificial(e.g., backup) lighting arranged over a grow area within the facility.

The system can also include a suite of fixed ambient sensors, such asincluding: an air speed sensor; a relative humidity sensor; atemperature sensor; and a light level sensor. For example, the suite offixed ambient sensors can be arranged adjacent the fixed overhead cameraover a grow area of the facility. The system can additionally oralternatively include multiple fixed suites of ambient sensors arrangedthroughout the grow area, such as suspended from ceiling or mounted onload-bearing columns within the facility and such as including one suiteof fixed ambient sensors per square meters of grow area.

5.1 Data Collection

The fixed optical sensor can regularly record images of the grow areabelow; and sensors in the suite of fixed ambient sensors can regularlycollect ambient data. For example, the fixed optical sensor can recordan image of once per 10-minute interval; the suite of fixed ambientsensors can record air speed, humidity, air temperature, and light levelvalues once per second; and the fixed optical sensor and the suite offixed ambient sensors can return these data to the computer system via awired or wireless connection.

5.2 Data Extraction from Global Image

Generally, because the field of a view of the fixed optical sensorcontains the entirety of the grow area or a large portion of the growarea housing multiple modules, wherein each module is occupied by themultiple individual plants, the per-plant resolution of an imagerecorded by the overhead camera may be relatively minimal. For example,for a four-megapixel optical sensor defining a field of view over aten-meter by ten-meter portion of the grow area housing a 4×8 grid arrayof two-meter by one-meter modules, each of these modules occupied by a4×12 array of plants at a nursery stage of development, a single plantin an image recorded by the fixed optical sensor may be represented in arelatively small 20-pixel by 20-pixel region of this image, which may beinsufficient to detect pests or calculate a size of the individual plantwith a high degree of accuracy.

However, the system can extract higher-level visual information fromthis image, such as generally whether multiple plants occupying a moduleare experiencing chemical burns, are overexposed to light, or arewilting. For example, upon receipt of an image from the fixed opticalsensor, the system can: implement computer vision techniques to detect afloor area represented in the image (e.g., represented by dark pixels orbackground pixels in the image); remove the floor area from the image;implement computer vision techniques (e.g., edge detection) to detectand distinguish discrete modules in the remaining region(s) of the image(e.g., represented by white and near-white pixels in the image; and scanthe remaining region(s) of the image for “green,” “brown,” and “yellow”pixels. The system can then: label “green” pixels within a known“healthy” green color range as representing healthy plant matter; label“green” pixels within a known “sub-healthy” green color range asrepresenting unhealthy plant matter; and label “brown” and “yellow”pixels as representing damaged or dead plant matter. For each moduledetected in the image, the system can: associate “green,” “yellow,” and“brown” pixels located within the detected perimeter of the module withthis module; estimate an average size of plants in the module based on aratio of the number of pixels labeled as plant matter to a total numberof pixels within the bounds of the module; and estimate a proportion ofunhealthy plant matter in the module based on a ratio of a sum of pixelslabeled unhealthy, damaged, and dead plant matter to a total number ofpixels labeled as plant matter within the bounds of the module.

For each module, the system can then compare this estimate of averageplant size derived from the current image recorded by the fixed opticalsensor to estimates of average plant size derived from similar regionsof preceding images recorded by the fixed optical sensor (over a periodof time in which the module was not known to have been moved by themover) to determine whether the average size of plants in the module hasincreased or decreased; if the system determines that the average sizeof plants in the module thus derived from this series of images hasdecreased, the system can flag the module as containing wilting plants.The system can similarly compare a proportion of unhealthy plant matterin the module estimated from the current image recorded by the fixedoptical sensor to proportions of unhealthy plant matter estimated fromsimilar regions of preceding images recorded by the fixed optical sensorto determine whether the proportion of unhealthy plant matter in themodule is increasing or decreasing; if the system determines that theproportion of unhealthy plant matter in the module thus derived fromthis series of images has increased, the system can flag the module asoverexposed (e.g., overexposed to light, heat, humidity, pH, ornutrients).

The system can therefore extract low-resolution visual informationcharacterizing the health and changes in the health of many plantsoccupying many modules in the field of view of the fixed optical sensorfrom images thus recorded by the fixed optical sensor.

5.3 Plant Association of Fixed-Infrastructure Data

The system can then associate these ambient and image-derived data withspecific plants known to be in modules within the grow area.

In one implementation shown in FIG. 2, the system maintains a module mapof last known locations of modules throughout the facility (e.g., acurrent (x,y) location of each module within a coordinate system definedfor the facility); as the mover moves a particular module throughout thefacility, such as according to a command issued by the system, thesystem can update this module map with new locations of this particularmodule (e.g., in real-time) based on locations returned by the moverduring this transfer. Upon receipt of an image from the fixed opticalsensor, the system can: dewarp the image; implement computer visiontechniques to segment the image into regions representing discretemodules in the grow area, as described above; calculate an (x,y)location of the center of each image segment based on a known positionand orientation of the fixed optical sensor within the facility (orbased on fixed fiducials detected in a region of the image representinga floor of the facility); and then, for each image segment, associatethe image segment with an identifier of a particular module based onproximity of the image segment to the last known location of thisparticular module stored in the module map.

The system can also maintain: one unique plant record for each plantoccupying a module in the facility, such as in the form of amulti-dimensional vector as described below and shown in FIG. 2; and aplant map linking each unique plant record to an identifier of a moduleand an address of a plant slot—in this module currently occupied by thecorresponding plant. (As the robotic manipulator at the transfer stationmoves a plant from one plant slot in one module to a plant slot inanother module or into a harvest container, etc., the system can updatethis plant map accordingly.) For a particular module identified in thecurrent image recorded by the fixed camera, the system can write atimestamp, an average plant size, a ratio of unhealthy to total plantmatter, a change in size or health of plants, and/or any otherquantitative data extracted from the image to each plant record ofplants known to occupy this module according to the plant map.

Alternatively, the system can detect optical fiducials on a moduledetected in an image received from the fixed optical sensor and align apreexisting slot map for a type of the module to these optical fiducialsto locate approximate centers of each plant slot—and therefore eachplant—in the module. For each plant slot thus detected for this module,the system can: associate a cluster of pixels in the image around thisplant slot with a plant; implement the foregoing methods and techniquesto derive a size, health, change in size, and/or change in health, etc.of a plant occupying this plant slot based on colors of pixels in thiscluster of pixels and/or based on differences between pixels in thiscluster of pixels and corresponding clusters of pixels in precedingimages recorded by the fixed optical sensor; and write theseplant-specific values to the plant record associated with this plantslot by the plant map.

The system can similarly write timestamped ambient sensor data receivedfrom the suite of fixed ambient sensors to plant records assigned toplants known to be proximal these sensors (i.e., based on the module mapand the plant map).

However, because fixed ambient sensors may be substantially remote froma module, global ambient sensor data collected by the suite of fixedambient sensors may loosely represent ambient conditions immediatelyadjacent plants occupying this module. Therefore, in one variationdescribed below, the system can calculate a temperature correctiongradient that transforms a single air temperature value read by a fixedtemperature sensor in the suite of fixed ambient sensors intotemperatures at locations across the grow area—such as as a function oftime of day and/or time of year—based on temperature data collected bythe mover while traversing the grow area over time. Upon receipt of anew temperature value from the fixed transfer station, the system can:input this new temperature value (and a current time of day and/or timeof year) into the temperature correction gradient to estimate a currenttemperature gradient across the grow area; project known locations ofmodules from the module map (or locations of specific plants based onthe module map and the plant map) onto the temperature gradientestimated for the grow area to estimate a temperature at each module (orat each individual plant) within the grow area; and write theseestimated temperature values to corresponding plant records. The systemcan implement similar methods and techniques to develop and implementair speed, humidity, and/or light level correction gradients and toestimate air speed, humidity, and/or light level values at each module(or at each individual plant) in the grow area based on these correctiongradients and new air speed, humidity, and/or light level data receivedfrom the suite of fixed ambient sensors.

Therefore, the system can: collect relatively low-resolution ambientsensor data and images of the grow area at a relatively high frequencythrough static infrastructure deployed within the facility; andregularly populate plant records—assigned to plants represented in theseimages and occupying modules known to be near these fixed sensors—withthese numerical-point data and other data extracted from these images.The system can thus form a dense historical record of low-resolutionplant characteristics and low-resolution ambient conditions around aplant for each plant grown in the facility and store these densehistorical records in corresponding plant-specific plant records.

(The system can additionally or alternatively include an autonomousaerial drone outfitted with an optical sensor and ambient sensors, asdescribed above, and configured to navigate throughout the facility,collect images of modules below, and collect ambient sensor dataaccording to similar methods and techniques.)

6. Mover

As described above and shown in FIGS. 2 and 4, the mover defines avehicle that autonomously: navigates throughout the facility;selectively arranges itself over (or adjacent) modules; collects sensordata from in and around these modules (e.g., images of plants in amodule, water quality data of water stored in the module, and/or ambientdata around the module); delivers modules to the transfer station fortransfer of plants into and out of these modules; and returns thesemodules to their assigned locations throughout the facility, such asresponsive to commands issued by the computer system.

6.1 Autonomous Navigation

As described below, the computer system generates commands to transportspecific modules to various locations throughout the facility, such as“module transfer requests” to transport modules between the grow area,transfer station, a cleaning station, a maintenance area, a dosingstation, and/or a quarantine station. The computer system can alsogenerate commands to collect optical, ambient, and/or water quality datafrom specific modules throughout the facility, such as “module scanrequests” to record module-level images, local air temperature andhumidity, and dissolved oxygen and water temperature in water stored inselect target modules. The computer system can distribute these commandsto the mover, and the mover can autonomously execute these commands.

In one implementation, the mover includes a gantry or boom. To engage amodule, the mover can: navigate to and align itself over the module,such as based on optical fiducials applied to the module; extend theboom into contact with the module; trigger a latch on the boom to engagethe module; and then retract the boom to lift the module. Alternatively,modules in the facility can include casters; to engage a module, themover can navigate up to and align itself with the module, trigger alatch to engage the module, and then push or pull the module—on itscasters—to another location within the facility (e.g., to the transferstation) before releasing the latch to disengage the module.

For the mover that is configured to autonomously navigate over a module,the mover can also include a downward-facing optical sensor. As themover approaches a module, the mover can: detect an opticalfiducial—located on the module—in the field of view of thedownward-facing optical sensor; and navigate toward the module to locatethis optical fiducial in a target position and/or orientation in thefield of view of the downward-facing optical sensor in order to alignitself to the module before triggering the boom or latch to engage themodule. However, the mover can implement any other method or techniqueto engage and manipulate a module.

6.2 Mobile Water Quality Sensors

The mover can also include a mobile suite of water quality sensorsconfigured to collect various water quality data of water stored in amodule.

Generally, water quality sensors may be expensive and require regularcalibration, which may increase complexity and cost of collecting suchwater quality data. The mover can therefore include a single mobilesuite of sensors and can collect water quality data from many modulesthroughout the facility over time, thereby limiting water monitoringinfrastructure costs, limiting complexity of calibrating and cleaningthese sensors, and distributing costs for these sensors and calibrationof these sensors across many modules in the facility over time.

In one example, the mover includes a water temperature sensor, a pHsensor, a nutrient level sensor (e.g., a dissolved oxygen, nitrogen, orother nutrient level sensor), and/or a water level sensor, all arrangedon a probe that is mounted to an actuator on the mover. In this example,the probe can be arranged on the mover such that the probe is aligned toan access window on a module (e.g., a spring-loaded window on the coverfloating in the open tray of the module) when the mover autonomouslyaligns itself over this module. Thus, upon arriving at and aligningitself to a module, the mover can extend the actuator to advance theprobe downward, through the window, and into the open tray of themodule, thereby bringing these water sensors into contact with waterstored in the tray. The mover can then read water quality (and quantity)values from these sensors and upload these data to the computer system(e.g., via a local wireless network).

In one variation, the mover also includes a sanitation block; betweenretracting the probe from one module and inserting the probe into a nextmodule according to module scan commands received from the computersystem, the mover can pass the probe into the sanitation block in orderto sanitize the probe, thereby reducing propagation of contaminants(e.g., spread of pests, such as algae) from one module to the next viathe probe as the mover navigates from one module to the next. Forexample, the sanitation block can include a steam chamber, vinegar wash,or UV-light box, and the mover can sanitize the probe after samplingeach module in the facility.

The mover can additionally or alternatively include a calibration blockcontaining a calibration fluid of known temperature, pH, and/or nutrientlevel (e.g., a dissolved oxygen, nitrogen). During operation, the movercan insert the probe into the calibration block and calibrate sensorsmounted to the probe according to known qualities of the calibrationfluid. For example, the calibration block can include a volume of pHcalibration fluid of known pH; after sanitizing the probe in thesanitation block described above, the mover can dry the probe (e.g.,with a blast of air), insert the probe into the pH calibration fluid,calibrate the pH sensor to the known pH of the pH calibration fluid, andrinse and dry the probe. The mover can repeat this process once per day,once per measurement of 50 modules, or on any other interval.

6.3 Mobile Ambient Sensors

The mover can also include a mobile suite of ambient sensors configuredto collect various ambient data from around a module.

In one implementation, the mobile suite of ambient sensors includes airspeed, relative humidity, temperature, and light level sensors, such asmounted to the chassis of the mover at a height that approximates aheight of plants growing in modules throughout the facility. In anotherexample, the mobile suite of ambient sensors can be mounted to the probedescribed above—above the water quality sensors—such that the mobilesuite of ambient sensors is located adjacent foliage of plants in amodule when the mover lowers the probe into a sampling position over themodule.

The mover can then read ambient conditions proximal this module fromthese ambient sensors and upload these data to the computer system(e.g., via the local wireless network).

Upon receipt of these local ambient data from the mover, the system can:reference the module map and plant map described above to identifyplants occupying this module; and then write these local ambientdata—and a corresponding timestamp—to plant records associated with eachof these plants.

6.4 Mobile Optical Sensor

The mover can also include a mobile optical sensor (e.g., a 2D or 3Dcolor camera, a 2D multispectral camera), such as mounted to the gantryand facing downward such that the full width and length of a module—andsubstantially all plants occupying the module—fall within the field ofview of the mobile optical sensor when the mover is aligned over themodule. Thus, upon arriving at a module during execution of a moduletransfer or module scan request, the mover can record a module-levelimage of the module below and upload this module-level image to thecomputer system (e.g., via the local wireless network).

6.5 Data Extraction from Module-Level Image

The system can then extract moderate-resolution visual information of agroup of plants occupying a particular module from a module-level imagethus recorded by the mobile optical sensor in the mover while the moverinteracts with this module.

In one implementation, the system extracts an approximate size (e.g.,width, diameter) of each plant in the module from this module-levelimage. In one example, the system implements computer vision techniques,such as edge detection or blob detection, to: identify a perimeter ofthe first module in the image; count a number of green pixels in theimage (e.g., a number of pixels exceeding a threshold intensity orbrightness in the green color channel in the image); and then authorizeplants in the first module for transfer to a second module if the ratioof green pixels to a total number of pixels in the image exceeds athreshold ratio. Similarly, the system can implement a color classifierto classify a likelihood that each pixel in the image represents plantmatter and then count the number of pixels in the image that areclassified as ‘likely to represent plant matter.’

The system can implement these methods and techniques for each colorchannel in the image, merge classifications for corresponding pixels ineach color channel, count a number of pixels in a composite color spacethat are ‘likely to represent plant matter,’ and identify clusters ofpixels ‘likely to represent plant matter.’ The system can also: detectan optical fiducial in the image; and align a preexisting plant slot mapfor the known module type of the module (e.g., stored in the module mapand confirmed by information stored in the optical fiducial) to theseoptical fiducials to approximate centers of each plant slot—andtherefore each plant—in the module represented in the image. For a firstplant slot in the first module thus located in the image, the system canthen: identify a cluster (or “blob”) of pixels ‘likely to representplant matter’ (e.g., a cluster of green pixels exceeding a thresholdintensity or brightness in the green color channel in the image)radiating from the approximate center of the first plant slot located inthe image; associate this cluster of pixels with a first plant in thefirst plant slot in the module; and estimate a size (e.g., anapproximate diameter) of the first plant based on a size and geometry ofthis cluster of pixels.

The system can also: implement methods and techniques similar to thosedescribed above and below to detect yellow and brown pixels near thiscluster of green pixels; and characterize a health of the first plantbased on presence of these yellow, brown, or dark pixels. In oneexample, the system implements template matching or object recognitiontechniques to identify leaves in the cluster of pixels associated withthe first plant in the module-level image, such as based on templateimages or models of leaves of plants of the same type and at similargrowth stages as the first set of plants in the first module. The systemcan then correlate light-colored (e.g., yellow, tan, brown) regionsaround perimeters of these leaves with chemical burns or insufficientnutrient supply and estimate a degree of chemical burns in the firstplant based on a proportion of light-colored leaf area to green leafarea in the region of the module-level image correlated with the firstplant. The system can implement similar methods and techniques to detectindicators of over- or under-exposure to temperature, humidity, light,nutrients, etc. based on presence of brown and yellow pixels and/or the“greenness” of green pixels in this region of the module-level image.

In another example, the system scans the module-level image for dark,discolored leaf regions that may indicate a nematode infestation. Inthis example, if the system detects any such dark, discolored leafregion in the first module, the system can calculate a proportion ofdiscolored leaf area to healthy leaf area (e.g., brown and yellow pixelsto green pixels) in the region of the image representing the firstplant. In particular, the resolution of the module-level image may beinsufficient to detect insects (e.g., ants, aphids, flies, silverfish,moths, or caterpillars, etc.) or other pests on the first plantdirectly. However, the system can scan the region of the module-levelimage associated with the first plant for indicators of pest pressure inthe first plant.

However, the system can extract any other characteristics or healthindicators for the region of the module-level image thus associated withthe first plant in this module.

The system can then: access the module map and plant map described aboveto identify a particular plant record corresponding to the first plant;write these data derived from the module-level image to the particularplant record; and/or write the segment of the image thus associated withthe first plant to the particular plant record.

The system can repeat this process for each other plant slot located inthe first module and update corresponding plant records accordingly.

6.6 Module-Level Scan Scheduling

The system can include a single mover (or small set of like movers)configured to navigate to individual modules throughout the facility andto collect optical, water quality, and ambient sensor data at eachmodule according to commands issued by the computer system. For example,the mover can collect ambient data, water quality data, and amodule-level image of a module during a module-level scan when the moverreaches the module and before moving the module while executing a moduletransfer request for this module. In this example, the mover canadditionally or alternatively execute a module scan of the module upondelivering the module to the target destination specified in the moduletransform request. In another example, the mover can collect ambientdata, water quality data, and a module-level image of a module during amodule-level scan when the mover reaches a module specified in a modulescan request; upon collecting these data, the mover can leave thismodule otherwise undisturbed and move on to a next module in a commandqueue maintained by the computer system.

Therefore, the mover (or a small set of movers in the facility) cancollect module-level data from each module in the facility on a regularbasis, such as once per day. Furthermore, though the mover may collectmodule-level data of an individual module less frequently than fixedinfrastructure sensors in the facility, these module-level data mayexhibit greater per-plant resolution than general data collected by theinfrastructure, thereby enabling the system to extract moresophisticated insights into each plant in the facility from thesemodule-level data (e.g., once per day).

6.7 Sensor Calibration

In one variation, the system calibrates high-frequency data collected bythe suite of fixed ambient sensors based on moderate-frequency,higher-resolution ambient data collected by the suite of mobile ambientsensors in the mover. In particular, because the mover locates the suiteof mobile ambient sensors immediately adjacent a module when recordingmodule-level ambient data, these module-level ambient data may betterrepresent true temperature, humidity, light level, and/or air flowconditions immediately adjacent plants in this module than ambient datacollected by the suite of fixed ambient sensors.

In one implementation, the system calculates a temperature correctiongradient that transforms a single air temperature value read by a fixedtemperature sensor in the suite of fixed ambient sensors intotemperatures at locations across the grow area. For example, uponreceipt of a first module-level temperature value from the mover whenthe mover is occupying a first location over a first module in the growarea at a first time, the system can pair this first module-leveltemperature value with a first general temperature value recorded by thefixed suite of ambient sensors at approximately the first time.Similarly, upon receipt of a second module-level temperature value fromthe mover when the mover is occupying a second location over a secondmodule in the grow area at a second time, the system can pair thissecond module-level temperature value with a second general temperaturevalue recorded by the fixed suite of ambient sensors at approximatelythe second time. By repeating this process in response to newtemperature data recorded by the mover and fixed infrastructure in thefacility over time, the system can amass a corpus of concurrentmodule-level data and generate temperature pairs. From these pairedtemperature data, the system can train a temperature correction gradient(or map, matrix, etc.) that outputs corrected local temperatureestimates at discrete module locations in the grow area given a singletemperature value read by the fixed suite of ambient sensors (and givena current time of day and/or time of year).

The system can implement similar methods to develop correction gradientsfor humidity, air speed, light level, and/or other ambient conditionsmonitored by the mover and fixed infrastructure in the facility.

The system can therefore leverage local, module-level ambient datarecorded by the mover at known locations throughout the grow area whilethe mover completes module transfer and module scan requests to developcorrection gradients that transform single-point ambient data collectedby fixed infrastructure in the facility into more accurate ambientconditions at discrete locations occupied by plants throughout the growarea. The system can then transform new general ambient single-pointdata recorded by fixed infrastructure in the facility into estimates oflocal ambient conditions at modules throughout the grow area and thenwrite these local ambient condition estimates to corresponding plantrecords based on module and plant locations stored in module and plantmaps described above.

However, the system can implement any other method or technique toleverage module-level data to calibrate general ambient data collectedby fixed infrastructure in the facility.

7. Transfer Station and Robotic Manipulator

As shown in FIGS. 3 and 5, the system also includes a transfer stationarranged within the facility and defining a location at which plants areautonomously inspected and transferred from a first module (e.g., anursery-type module) containing a higher density of plants slots to asecond module (e.g., a finishing module) containing a lower density ofplants slots. The system can also include a robotic manipulator:arranged at the transfer station; defining a multi-link roboticmanipulator that is sufficiently mobile to reach each plant slot in amodule temporarily positioned at the transfer station; including an endeffector configured to engage plant cups supporting plants in thismodule; and/or including an optical sensor (e.g., a multi-spectralcamera, or a stereoscopic camera, etc.) configured to recordplant-specific images of plants in these modules, as described below.

In particular, the robotic manipulator functions to transfer plantsbetween a first module (e.g., a nursery-type module) exhibiting a firstdensity of plant slots to a second module (e.g., a finishing-typemodule) exhibiting a second density of plant slots less than the firstdensity. By autonomously moving plants from high-density modules tolower-density modules, the robotic system can ensure that plants havesufficient access to light, water-borne nutrients, and space to continuegrowing over time. While sequentially transferring single plants betweenmodules, the robotic manipulator can also collect optical andnumerical-point data from each, singular plant.

For example, the mover can deliver a nursery-type module to the roboticmanipulator at a first time to receive a set of seedlings from a seedingtray; when transferring seedlings into the nursery-type module, therobotic manipulator can record images and weight data for theseseedlings. The mover can then return the nursery-type module to itsassigned location within the facility. Two weeks later, the mover canreturn the nursery-type module to the robotic manipulator; the roboticmanipulator can then collect images and weight data from these plantswhile transferring these plants from the nursery-type module to afinishing-type module. The mover can then return the finishing-typemodule to its assigned location within the facility. Two weeks later,the mover can return the finishing-type module to the roboticmanipulator; the robotic manipulator can then collect images and weightdata from these plants while transferring these plants from thefinishing-type module into boxes for final processing, packaging, andshipment from the facility. The robotic manipulator can thereforecollect high-resolution image and weight data from plants at a lowfrequency (e.g., once per two-week interval); the computer system canthen write these high-resolution, low frequency data to plant recordsassigned to corresponding plants.

7.1 Images

In one implementation, the robotic manipulator includes an articulableoptical sensor (e.g., a 2D or 3D color camera or multispectral imager)integrated into or otherwise coupled to the end effector. Upon arrivalof a first module at the transfer station, the robotic manipulator cannavigate to a position that orients the articulable optical sensor overthe first module and then trigger the articulable optical sensor torecord a single, 2D photographic image (or “optical scan”) of all plantsin the first module. Alternatively, upon arrival of the first module,the robotic manipulator can: navigate the articulable optical sensorthrough multiple preset positions, such as one position over each plantslot in the module; and record a 2D photographic image through thearticulable optical sensor at each of these positions. For each of theseimages, the system can then: query a module map and a plant map of thefacility to link an image recorded by the robotic manipulator to asingular plant record; implement methods and techniques described aboveand below to extract characteristics of a singular plant represented inthis image; and store these characteristics (and the image) in thecorresponding plant record.

Alternatively, the system can include a 2D or 3D optical sensor (e.g., a3D scanner) arranged near the robotic manipulator; after removing aplant from the first module, the robotic manipulator can orient theplant proximal the optical sensor, and the optical sensor can record a2D or 3D scan of the plant before the robotic manipulator places theplant in the second module. For example, the robotic manipulator canorient a plant such that its anteroposterior axis faces the opticalsensor at a distance from the optical sensor sufficient to keep the topof the plant's foliage and the bottom of the plant's root structure inthe optical sensor's field of view. The optical sensor can thus recordan image of the side of this plant. The system can then extractcharacteristics of both the plant's foliage and the plant's rootstructure from this image of the side of the plant.

In the foregoing implementation, the robotic manipulator can alsomanipulate the plant within the field of view of the adjacent opticalsensor while the optical sensor records additional 2D or 3D images ofmultiple faces of the plant; and the system can stitch these 2D or 3Dimages into a composite, high-resolution 3D image of the entire plant.

However, the robotic manipulator can include or interface with anoptical sensor of any other type at the transfer station and canimplement any other method or technique to collect optical data of anindividual plant when transferring this plant between two modules.

7.2 Weight

In one implementation, shown in FIG. 3, the robotic manipulator includesa set of (e.g., three) strain gauges arranged across three perpendicularaxes at a junction between the end effector and an adjacent arm segment(or between the end effector and jaws extending from the end effector).Prior to selecting a first plant from a module nearby, the roboticmanipulator can pause its motion, sample the set strain gauges, andstore values read from the strain gauges as a tare value in order tocalibrate these strain gauges. Once the end effector removes a plantfully from its slot in the module, the robotic manipulator can againsample the set of strain gauges and calculate a weight of the plantbased on differences between new strain values read from the straingauge and stored tare values.

In this implementation, after removing a plant from its slot in themodule, the robotic manipulator can read the strain gauges immediatelyand calculate a weight of a plant accordingly from these data. Thesystem can also multiply this weight value by a wetness coefficient inorder to correct the calculated weight of the plant for wetness ordampness of its roots. Additionally or alternatively, once the plant isremoved from its slot, the robotic manipulator can: rapidly oscillatethe end effector to shake water from the plant's roots into a collectioncanister below; activate a blower adjacent the plant to blow water fromthe plant's roots (and away from the module), such as down or laterallyinto a collection canister); or pause its motion with the plant over acollection canister as water drips from the plant's roots prior tosampling the strain gauges in the robotic manipulator.

However, the robotic manipulator can include any other type of sensorarranged in any other location and configured to output a signalrepresentative of a weight of the plant. The robotic manipulator canalso implement any other methods or techniques to calibrate the sensorprior to retrieving the first plant and to interpret a signal read fromthe sensor as a weight (or mass) of the first plant.

7.3 Image Feature Extraction

Upon receipt of a high-resolution image of an individual plant recordedat the transfer station, the system can extract plant characteristicsfrom the image and write these characteristics to the plant recordassigned to this plant.

7.3.1 Plant Size

In one implementation, the system implements computer vision techniquesto: detect a boundary of a plant represented in an image; calculate anumber of green pixels in the image of a plant (e.g., a number of pixelsexceeding a threshold intensity or brightness in the green color channelin the image), which may be indicative of the size of the plant; and/orgenerate a metric representing a “greenness” of a region of the imagecorresponding to the plant, which may be indicative of the health of theplant. For example, the system can: implement a color classifier toclassify a likelihood that pixels in the image are likely to representplant matter and then count the number of pixels in the image that areclassified as ‘likely to represent plant matter’; or implement thisprocess for each color channel in the image, merge classifications forcorresponding pixels in each color channel, count a number of pixels ina composite color space that are ‘likely to represent plant matter,’ andrepresent the size of the plant based on this pixel count or fillfactor—that is, a proportion of foliage or leaf cover area representedin the image. The system can then write these metrics to the plantrecord assigned to this plant.

7.3.2 Pests

In another implementation, the system can implement computer visiontechniques to detect pests affecting a plant shown in an image. Forexample, the system can scan the image for dark (e.g., black or brown)round or lozenge-shaped spots that may indicate the presence of insects(e.g., ants, aphids, flies, silverfish, moths, or caterpillars, etc.) onthe plant. In this example, the system can maintain a counter of suchspots (e.g., spots between a minimum and maximum dimension or area) andflag this plant as infected by a pest if the final value of this counterexceeds a threshold value. Furthermore, the robotic manipulator canrecord a sequence of images of a plant while transferring the plant fromone module to the next—or record multiple images of the full length andwidth of a module—over a period of time (e.g., five seconds), repeat theforegoing methods and techniques to identify such spots in each image inthis sequence, implement object tracking techniques to track motion ofthese spots across these images, confirm that these spots representinsects if these spots are determined to have moved throughout thissequence of images, and then flag the plant as infected by thisaccordingly. The system can implement similar methods and techniques todetect parasites (e.g., chiggers, ticks, or mites) and/or gastropods(e.g., slugs) on leafy regions of plants shown in the image.

In another example, the system scans the image for dark, discolored leafregions that may indicate a nematode infestation. In this example, ifthe system detects any such dark, discolored leaf region in the image ofa plant or calculates that a proportion of the leaf area in the plant isso discolored, the system can flag the plant as infected by such a pest.However, the system can implement any other methods or techniques todetect pests directly or to infer the presence of pests in plants in thefirst module from color data contained in one or a sequence of images ofthe first module, such as based on images of the first module collectedby the fixed infrastructure, the mover, or by the robotic manipulator.

The system can then write features extracted from an image of a plantand suggestive of a pest and/or a pest pressure flag to a plant recordassigned to this plant.

7.3.3 Leaf Color and Leaf Area

The system can also extract color values from regions of the image thatcorrespond to leaves or other plant matter and transform these data intoa representation of health of the plant shown in the image.

In one implementation, the system implements template matching or objectrecognition techniques to identify leaves in the image, such as based ontemplate images or models of leaves of plants of the same type and atsimilar growth stages as the group of plants in the first module. Thesystem can then correlate light-colored (e.g., yellow, tan, brown)regions around perimeters of these leaves with chemical burns orinsufficient nutrient supply. The system can also correlatelight-colored, low-gloss foliage with heat burns. The system can thenwrite the defect and perceived degree of this defect to the plant recordassigned to the plant shown in this image.

The system can also estimate a total leaf area of the plant based on aquantity of green pixels (or a quantity of pixels likely to representplant matter) in the high-resolution image of the plant. For example,the system can: access a scalar value based on a known offset from therobotic manipulator to the plant slot containing the plant when thehigh-resolution image of the plant was recorded; and then multiply thequantity of green pixels in the image by this scalar value to estimate atotal leaf area of the plant. The system can then calculate a foliagedensity of the plant based on a ratio of this leaf area to the estimatedsize of the plant described above, which may correlate to health orviability of the plant.

However, the system can implement any other method or technique toestimate the total leaf area of the plant and/or to characterize healthof the plant based on its total leaf area. Furthermore, the system canextract, characterize, and/or label any other singular features ormulti-dimensional feature sets from an image of a plant and write thesedata to a corresponding plant record.

8. Optical Inspection Station

In one variation shown in FIG. 5, the system includes an opticalinspection station arranged at the transfer station and physicallyaccessible to the robotic manipulator. In one implementation, theoptical inspection station includes: an enclosure defining an apertureconfigured to receive a plant via the robotic manipulator; a receptaclearranged inside the enclosure and configured to receive and support aplant cup within the enclosure; a set of light elements configured torepeatably illuminate a plant placed over the receptacle by the roboticmanipulator; and an optical sensor (e.g., a 2D color camera, astereoscopic color camera, and/or a multispectral camera, etc.) arrangedinside the enclosure over the receptacle and configured to capture a 2Dor 3D image of the plant while illuminated by the light elements. Inthis implementation, the optical inspection station can also include: arotary table arranged inside the enclosure, supporting the receptacle,and configured to rotate a plant placed in the receptacle while theoptical sensor records a sequence of images of the plant.

In the foregoing implementation, upon retrieving a plant from a moduletemporarily positioned in the module docking location, the roboticmanipulator can: deposit the plant onto the receptacle inside theoptical inspection station; and then retract the end effector beforetriggering the optical inspection station to execute a scan routine.During a scan routine, the optical inspection station can: activate thelight elements; and trigger the optical sensor(s) to record 2D or 3Dimages of the plant, such as while the rotary table rotates 360° withinthe optical inspection station.

Alternatively, the optical inspection station can include an enclosure,a window, a rotary table, and light elements over the rotary table. Uponretrieving a plant from a module temporarily positioned in the moduledocking location, the robotic manipulator can: place the plant on therotary table within the optical inspection station; orient thearticulable optical sensor on the end of the robotic manipulator to facethe plant now located in the optical inspection station; trigger thelight elements to activate while the rotary table rotates the plant; andrecord images of the plant—through the articulable optical sensorarranged on the robotic manipulator—to record images of the plant; andthen retrieve the plant from the optical inspection station beforedepositing the plant into an assigned plant slot in another module.

8.1 Selective Scanning at the Optical Inspection Station

In this variation, the system can scan select plants in the opticalinspection station. For example, the robotic manipulator can record onehigh-resolution plant-level image of each plant in a module—through thearticulable optical sensor arranged on the end of the roboticmanipulator—for each module delivered to the transfer station by themover. However, in this example, the robotic manipulator can insert onlyone plant (or fewer than 10% of plants) per module into the opticalinspection station and thus record ultra-high-resolution plant-levelimages of only one plant (or fewer than 10% of plants) per moduledelivered to the transfer station. In particular, retrieving a plantfrom a module, locating the plant in the optical inspection station,recording an ultra-high-resolution plant-level image of this plant, andthen retrieving this plant from the optical inspection station mayrequire significantly more time for the robotic manipulator to completethan the robotic manipulator recording an image of the plant with thearticulable optical sensor while simultaneously retrieving the plantfrom the module; the ultra-high-resolution plant-level image of theplant may also require significantly more processing resources toanalyze than the high-resolution plant-level image recorded by thearticulable optical sensor on the robotic manipulator.

The system can therefore record ultra-high-resolution plant-level imagesof select plants in order: to limit time spent transferring plants fromone module to another, thereby enabling one robotic manipulator toservice many (e.g., hundreds, thousands of) modules, each containingmany (e.g., 12 or 48) plants; and to limit data storage and dataprocessing load while also enabling the system to accessultra-high-resolution data of select plants that may be representativeof many other plants occupying the same module (and even representativeof many other plants occupying other modules located nearby in the growarea).

However, the system can implement any other methods or techniques to:select a representative plant from a group of plants in one or moremodules; to trigger the transfer station to record a three-dimensionaloptical scan of the representative plant; and to extract anultra-high-resolution interim or final outcome of the representativeplant from the three-dimensional optical scan.

8.2 Image Feature Extraction

The system can then implement methods and techniques similar to thosedescribed above to extract various characteristics of a plant from thisultra-high-resolution image, such as: the size, shape, and color of theplant; pest indicators; indicators of over- and under-exposure totemperature, humidity, light, nutrients, etc.; and/or leaf area orfoliage density; etc. In particular, because the image recorded by theoptical inspection station (or by the optical inspection station)contains a very high density of optical data of a plant relative toimages recorded by the fixed infrastructure, the mover, or by therobotic manipulator when retrieving the plant from a module, the systemcan extract relatively high-precision, relatively low-error quantitativeplant metrics from the ultra-high-resolution image.

As described above, the system can store these high-precision, low-errorquantitative plant metrics in a plant record assigned to thecorresponding plant.

9. Lab Testing and Consumer Feedback

In one variation shown in FIG. 1, physical whole or partial plantsamples are collected from plants and lab tested: to determinecomposition (e.g., nutrient composition) of these plants; to identifypests or disease vectors in these plants; and/or to qualify flavorprofile or aroma of these plants. In particular, the system can flag asmall subset of plants for lab testing, such as one plant in onethousand viable plants harvested in the facility. The system can thustrigger selective collection of plant metrics in additionaldimensions—such as composition, pathogen vector, aroma, and/orflavor—from a relatively small proportion of plants grown in thefacility.

In one example, a human operator manually collects (e.g., cuts) physicalsamples from plants flagged by the system, labels these samples, andpackages these samples for lab testing. Alternatively, the roboticmanipulator can automatically collect physical plant samples from selectplants over time, such as responsive to plant sample requests issued bythe computer system. For example, the robotic manipulator can include anexchangeable cutting end effector including a blade or scissorconfigured to cut samples from plants. When the system flags a plant forlab testing (e.g., every 1,000^(th) plant) and a module containing thisplant is delivered to the robotic manipulator, the robotic manipulatorcan: autonomously engage the cutting end effector; cut a sample from theflagged plant; dispense this sample into a labeled lab test package;retrieve the standard end effector for transferring plants betweenmodules; and resume transferring plants out of this module and into anadjacent module. Alternatively, the robotic manipulator can remove anentire plant from a module and place this plant in a separate labeledlab test package. These whole or partial plant samples can then bedelivered to a test facility, such as an external lab, for testing.

Upon receipt of lab results for a sample—such as including composition,pest or disease vector, aroma, and/or flavor profile of this sample—thesystem can write these lab results to the plant record associated withthis plant.

The system can additionally or alternatively flag a particular plant foradditional human labeling. For example, the system can prompt a human toindicate visual appeal of a particular plant through a grower portalexecuting on a computing device. In this example, the system can promptthe human to provide this feedback for a particular plant whileoverseeing automated transfer of many plants out of a module at thetransfer station. Alternatively, the system can serve a prompt toprovide an indicator of visual appeal (e.g., on a scale from “0” to “10”or on a scale from “unsalable” to “very saleable”) for a particularplant through the operator portal and include a location of the moduleand the address of the plant slot in this module that is currentlyoccupied by the particular plant. The system can then write the human'sfeedback response to this prompt to the plant record associated withthis plant.

The system can also prompt a human to indicate ideal size, shape, aroma,flavor profile, and/or nutrient composition. For example, upon receiptof aroma, flavor profile, and/or nutrient composition test results for aparticular plant from an external lab, the system can prompt a humanoperator in the facility to indicate proximity of the aroma, flavorprofile, and/or nutrient composition to target values; and the systemcan store this feedback in a plant record associated with the particularplant.

However, the system can collect any other human feedback and/or externaltest data for select plants grown within the facility.

10. Aggregating Plant Data

Therefore, the system can: extract low-resolution optical information(e.g., plant size, plant color) for each plant in the facility fromimages recorded by the fixed optical sensor at a relatively highfrequency (e.g., once per ten-minute interval) as these plants grow overtime (e.g., over an eight-week growth cycle); extractmoderate-resolution optical information for each plant in the facilityfrom images recorded by the mover at a moderate frequency (e.g., onceper day) as the mover interfaces with individual modules within thefacility according to module transfer and/or module scan requests; andextract relatively high-resolution optical information for each plant inthe facility from images recorded at the transfer station at arelatively low frequency (e.g., once per two-week interval) as therobotic manipulator moves individual plants into and out of modules. Thesystem can store these plant characteristics—such as plant size (e.g.,height, width), color, leaf area, and/or foliage density—derived fromlow-, moderate-, and high-resolution images recorded during growth of anindividual plant as interim outcomes of this plant in the plant'sassigned file. For example, the system can store each interim outcome ofa plant with a timestamp based on a time since the plant was firstplanted in a seeding tray or first loaded into a nursery-type module(hereinafter a “reference date”). In this example, the system can alsoweight or prioritize these interim outcomes based on the quality (e.g.,the resolution) of the image from which these interim outcomes werederived, such as by assigning a lowest weight to interim outcomesextracted from low-resolution images collected by the fixedinfrastructure and a greatest weight to interim outcomes extracted fromoptical data collected by the optical inspection station.

The system can similarly store plant characteristics derived from ahigh-resolution image and/or an ultra-high-resolution image—recorded atthe transfer station when a plant is harvested—as a final outcome ofthis plant in the plant's assigned file.

The system can also store indicators of pest pressures—derived fromthese low-, moderate-, and/or high-resolution images—as growth inputparameters for these plants represented in these images. For example,because presence of pests may affect growth of a plant and the plant'sfinal outcome, such as by reducing access to nutrients or by consumingplant matter, the system can store a probability of pest presence, atype of pest present, and/or a magnitude of pest infestation for anindividual pest—derived from low-, moderate-, and/or high-resolutionimages of this plant—as growth input parameters in the plant's assignedfile. The system can also timestamp these pest-related growth inputparameters based on times that these images were recorded, as describedabove.

Similarly, the system can store ambient condition and water quality datacollected by the fixed infrastructure and the mover during growth ofplants in the facility as growth input parameters for these plants. Forexample, the system can write ambient condition and water quality growthinput parameters to a plant record associated within a particular plant,including a timestamp representing a time that these data were collectedafter a reference time of the particular plant and including a weight orpriority of these data (e.g., a low weight if recorded by fixedinfrastructure and higher weight if recorded by the mover).

10.1 Data Structure

Therefore, as optical and numerical-point data uniquely representingmany (e.g., thousands, millions) of plants within the facility arecollected over time, the system can fuse these data into uniquemulti-dimensional temporal representations of these plants, including:inputs that affect growth of a plant; states of the plant at distinctintervals throughout its growth cycle (or “interim outcomes”); and afinal outcome of the plant, such as its health, visual appeal (e.g.,color and size), flavor profile, and nutrient composition upon theconclusion of the growth cycle.

In one implementation, the system handles various timestampednumerical-point data collected over a plant's growth cycle as inputparameters affecting growth of the plant, such as including: plantcultivar (or “strain”) identifier; ambient light level; supplementallight level; total light level; air speed; relative humidity; ambienttemperature; water temperature; water pH; dissolved nutrient (e.g.,oxygen, nitrogen) levels; etc.

In a similar example, the system can calculate: average air temperaturesproximal the plant over discrete time intervals of one hour over aneight-week growth cycle after the plants reference time; average ambienthumilities proximal the plant over discrete time intervals of four hoursover the eight-week growth cycle; variance of dissolved oxygen in themodule occupied by the plant over discrete time intervals of one dayover the eight-week growth cycle; variance of pH in the module occupiedby the plant over discrete time intervals of twelve hours over theeight-week growth cycle; and variance of nutrient level in the moduleoccupied by the plant over discrete time intervals of two days over theeight-week growth cycle; etc. The system can write these values tocorresponding predefined element positions in the plant's vector to forma dense quantitative representation of the plants exposure to variousinput parameters over the plant's eight-week growth cycle. In thisexample, the system can similarly represent interim outcomes of theplant in absolute or relative quantitative values on daily intervals inthe plant's vector.

The system can further represent presence, type, and/or magnitude ofpest pressure detected at a plant as quantitative values and write thesevalues as input parameters in the plant vector. As additionalnumerical-point data are collected over time for the plant and stored ina plant record assigned to this plant, the system can aggregate thesedata into a multi-dimensional vector representing these input parametersover the plant's growth cycle.

The system can also label the multi-dimensional vector representing theplant with plant outcomes. For example, the system can: extract a finalfoliage size, final foliage shape (or “fullness”), final root size, anumber of leaves, and final foliage color of a plant from a final imageof the plant recorded by the robotic manipulator prior to processing,packaging, and shipment of the plant from the facility; and then labelthe corresponding vector with these outcomes. When available for theplant, the system can also retrieve flavor profile test data, lab result(e.g., nutrient) data, final weight, and either acceptance or rejectionof the plant by a buyer and label the vector with these data. The systemcan thus construct a single multi-dimensional vector representingmultiple input parameters and labeled with multiple outcomes for oneplant grown in the facility. In particular, the vector: can containquantitative metrics of the plant's exposure to multiple inputs overtime during its growth; can be labeled with the plant's outcome; and canthus represent a link between input parameters, interim outcomes, and afinal outcome for this unique plant.

(Alternatively, the system can fuse select outcomes into a quantitativeor qualitative measure of the success of the plant. For example, thesystem can calculate a linear combination of the outcomes according topreset weights, wherein the value of the linear combination is directlyproportional to the plant's final size, proximity to a target color,proximity to a target shape, proximity to a target nutrient level, andsalability and is inversely proportional to a final size of the plant'sroots (which, when large, may impede transfer between modules andrepresent waste), the plant's growth rate (e.g., overall size per unittime of its growth cycle), and time from packaging to sale of the plant.The system can then label the vector with this singular metric of “plantsuccess.”)

The system can also associate the vector with quantitativerepresentations of interim outcomes of the plant, such as extracted fromlow-, moderate, and high-resolution images and/or from operator feedbackrecorded throughout the plant's growth cycle. As described below, thesefeatures can later be compared to features of a new plant—similarlyextracted from images of the new plant recorded during its growthcycle—to determine the growth trajectory of the new plant, to predictthe new plant's final outcome (e.g., final size, final weight, finalcolor, final flavor profile, and/or salability, etc.); and to informadjustment of input parameters for the new plant in order to align itsgrowth trajectory to that of a previous plant with a known positivefinal outcome.

The system can implement the foregoing processes for each plant grown inthe facility in order to generate a (large) set of uniquemulti-dimensional vectors representing unique plants and labeled withfinal outcomes of these unique plants.

However, the system can implement any other methods or techniques topackage data—including input parameter data and outcomes derived fromdata collected over the growth cycle of a plant—into a data container.The system can then compare this data container to data containersrepresenting other plants grown in the facility to isolate links betweeninput parameters and outcomes of plants grown in the facility and torefine a grow schedule for these plants accordingly.

11. Correlation: Input Parameters and Final Outcomes

Once the system has generated this set of multi-dimensional vectorsrepresenting various input parameters and known outcomes of plants grownin the facility, the system can derive correlations between ranges ofinput parameters and various outcomes.

In one implementation shown in FIG. 2, the system implements structureddata analysis (e.g., linear regression analysis), cluster analysis,and/or other statistical analysis and machine learning techniques toquantify correlation between various input parameters and: plant size;plant weight; plant shape; plant color; flavor profile; nutrientcomposition; and/or visual appeal; etc. for plants grown in thefacility. For example, the system can identify temporal ranges of inputparameters—over the course of the growth cycle of a plant—that exhibitstrong correlation to: at least a minimum plant size; at least a minimumplant weight; a plant shape within a target shape window; a plant colorwithin a target color range; a flavor profile within a profile range;and/or a nutrient composition within a target composition range at theconclusion of the growth cycle.

11.1 Weighted Outcomes

In this implementation, the system can also assign greater weights orpriorities to select final outcomes. For example, the system can: assigna first weight to a target color range, since color may be a greatestinitial attractant to produce for a customer; assign a second weightless than the first weight to a target size range, since larger produceunits may be more attractive to consumers but may be more difficult topackage up to a threshold size; assign a third weight less than thesecond weight to flavor profile, since flavor—which may be informed bynutrient composition—may affect repeat purchases by consumers and sinceflavor consistency may be valued by consumers; and assign a fourthweight less than the third weight to smaller root structures, sincesmaller root structures ease transfer of plants between modules, easemodule cleaning, and reduce plant waste.

11.2 Regression Techniques

The system can implement linear regression techniques to quantifystrengths of relationships between various input parameters to which aplant is exposed over time (e.g., an eight-week growth cycle) and theoutcome of the plant based on similarities and differences of timeseries input parameters and outcomes stored in these many (e.g.,thousands, millions of) multi-dimensional vectors representing plantsgrown in the facility over time.

In one implementation, the system can identify ranges of inputparameters—occurring over time—that yield a suitable compromise of theseweighted outcomes. For example, the system can: identify a group ofvectors associated with a group of plants exhibiting near-optimumcompromises of the weighted outcomes or representing near-maximizationof the weighted outcomes; identify a first set of inputparameters—represented by vectors in this first set—that exhibit strongsimilarities (e.g., narrow distribution of values) and define nominaltarget values and tight tolerances for input parameters in this firstset accordingly; identify a second set of input parameters—representedby vectors in this second set—that exhibit moderate similarities (e.g.,moderate distribution of values) and define nominal target values andmoderate tolerances for input parameters in this second set accordingly;and identify a third set of input parameters—represented by vectors inthis third set—that exhibit wide differences (e.g., large distributionof values) and define nominal target values and loose tolerances forinput parameters in this third set accordingly. The system can implementsimilar methods for each input parameter independently and thencalculate nominal target values (e.g., based on time from plant seeding)and define tolerances proportional to distribution of the inputparameter across the set of vectors for “ideal” plants in order togenerate a grow schedule that specifies target or target ranges of:natural light; supplemental light; total light; air speed; relativehumidity; ambient temperature; water temperature; water pH; dissolvednutrient (e.g., oxygen, nitrogen) levels; etc. over time in order toachieve “ideal” plants.

In another implementation, the system implements clustering techniques(e.g., k-means clustering techniques) to group subsets of many (e.g.,thousands, millions of) multi-dimensional vectors—representing plantsharvested at the facility over time—by similarity of known finaloutcomes (e.g., weight, diameter, height, color, pest presence). For acluster of vectors representing plants with like outcomes, the systemcan then derive strengths of relationships between input parametervalues (e.g., time series of discrete input parameters values, inputparameters ranges, or input parameter variance) stored in these vectorsand a final plant outcome represented by this cluster based onsimilarities and differences between input parameter values stored inthese vectors. The system can repeat this process for each other clusterof vectors. By comparing derived relationships between input parametersand final plant outcomes across multiple clusters representing plants ofdifferent final outcomes (e.g., different sizes, geometries, colors,flavor profiles, etc.), the system can further refine these derivedrelationships.

Alternatively, the system can implement clustering techniques to groupsubsets of many multi-dimensional vectors—representing plants harvestedat the facility over time—by similarity of input parameter valuescontained in these vectors. The system can label these clusters withplant outcomes, such as: human-supplied visual appeal, flavor profile,and/or composition feedback; human-supplied indication of proximity totarget visual appeal, flavor profile, and/or composition; and/or size,shape, color, weight, and/or geometry outcomes extracted from datacollected at the transfer station when these plants were harvested. Thesystem can then implement regression techniques to derive strengths ofrelationships between input parameter values and these final outcomesbased on similarities of input parameter data stored in vectors in onecluster and differences in input parameter data stored in vectors inthis cluster and stored in vectors in other clusters. The system canadditionally or alternatively calculate variance in final outcomes basedon differences in final outcomes associated with clusters of vectorsrepresenting very similar input parameters through the growth cycles ofcorresponding plants grown in the facility.

The system can therefore derive strengths of relationships between: eachof [plant cultivar, ambient light level, supplemental light level, totallight level, air speed, relative humidity, ambient temperature, watertemperature, water pH, and/or dissolved nutrient level, etc.] eachsecond, minute, hour, day, or week over a growth cycle of a plant; andeach of the [size, shape, weight, geometry, color, flavor, aroma,composition, and/or pest presence, etc.] of the plant at the conclusionof the growth cycle. The system can store these relationships betweeninput parameters and final outcomes in a plant model for plants grown inthe facility, as shown in FIG. 2.

11.3 Default Grow Schedule

Based on this plant model, the system can isolate time series ofdiscrete input parameter values, input parameters ranges, and/or inputparameter variances for various input parameters over a growth cycle ofa plant that are likely to yield: “ideal” plants; “sufficient” plants;and “rejected” plants. The system can then construct a default growschedule that defines: a time series of target input parameter valuesand permissible ranges (or variances, tolerances) for these inputparameters; and a schedule for planting seeds, transferring seedlingsinto nursery modules, transferring plants from nursery-type modules intofinishing modules, and harvesting plants from finishing-type modulesthat, when implemented over a growth cycle of a plant, is predicted toyield an “ideal” plant (or at least a “sufficient” plant of suitablesize, weight, geometry, color, and/or flavor profile, etc. and not a“rejected” plant).

The system can implement closed-loop controls: to autonomously driveambient humidity, ambient temperature, ambient light level, and windspeed over each module and to drive water temperature, water pH, andnutrient level within each module toward this default grow schedulebased on low-, moderate-, and high-resolution ambient, local, andplant-specific data collected by infrastructure in the facility overtime; and to autonomously trigger the module and transfer station tocooperate to transfer and harvest plants from modules in order to growmany “ideal” plants with limited human input or oversight.

However, the system can implement any other methods or techniques totransform labeled multi-dimensional vectors—representing plants grown inthe facility—into a grow schedule defining input parameter ranges forgrowing “ideal” plants in the facility.

11.4 Additional Relationships and Modified Grow Schedules

The system can implement similar methods and techniques to derivestrengths of relationships between: each of [plant cultivar, ambientlight level, supplemental light level, total light level, air speed,relative humidity, ambient temperature, water temperature, water pH,and/or dissolved nutrient level, etc.] each second, minute, hour, day,or week over a growth cycle of a plant; and each of the [size, shape,weight, geometry, color, composition, and/or pest presence, etc.] of theplant at certain instances within the plant's growth cycle—such as dailyor bi-weekly—based on interim outcomes extracted from low-, moderate,and high-resolution images of the plant. In particular, the system canimplement clustering, regression, and/or other methods and techniquesdescribed above to derive strengths of relationships between: interimoutcomes of a plant in various dimensions (e.g., size, shape, weight,geometry, color, and/or pest presence) at certain times in the growthcycle of the plant; and various input parameters to which the plant isexposed from the reference date of the plant up to these times in thegrowth cycle of the plant.

The system can also implement similar methods and techniques to derivestrengths of relationships between interim outcomes of plants and finaloutcomes of plants. In particular, the system can: identify interimoutcomes in certain dimensions (e.g., sizes below a threshold size,colors outside of a target color range, or pest infestation above athreshold degree of pest infestation) that are strong indicators of poorfinal outcomes (e.g., poor visual appeal, underweight outcome, undersizeoutcome, poor flavor profile, poor aroma); and identify other interimoutcomes in certain dimensions that are strong indicators of positivefinal outcomes. The system can then autonomously shift resources from afirst group of plants exhibiting interim outcomes indicating poor finaloutcomes to a second group of plants exhibiting interim outcomesindicating positive final outcomes. For example, the system can isolatecertain interim outcomes for young (e.g., two-week-old) plants thatexhibit strong correlation to poor final outcomes (e.g., underweight,poor visual appeal, pest presence). Upon receipt of a global,module-level, or plant-specific image of a new plant, the system canextract an interim outcome of this new plant from the image. If thisinterim outcome of the new plant sufficiently approximates an interimoutcome previously associated with a poor final outcome in one or moredimension, the system can flag the new plant to be culled from the growarea in order to reduce resource load (e.g., light, nutrients, airquality control) that may otherwise have been allocated to grow thisplant to a poor final outcome. The system can then dispatch the mover todeliver a module containing this new plant to the transfer station—suchas immediately or according to a next scheduled transfer for thismodule—and prompt the robotic manipulator to remove the new plant fromits module and to discard the new plant into a compost bin.

In another example, the system can identify an interim outcome thatexhibits weak correlation to either positive or poor outcomes. Givensuch understanding of weak correlation between this interim outcome anda final outcome, the system can then identify plants exhibiting suchinterim outcomes and reallocate resources to these plants (e.g., morefrequent monitoring with the mover and transfer station, more frequentwater and nutrient replenishing, relocation to a grow area with tighterambient controls, etc.) in order to drive these plants toward positiveoutcomes.

In yet another example, the system can identify an interim outcome thatexhibits strong correlation to positive final outcomes. Given suchunderstanding of strong correlation between this interim outcome and afinal outcome, the system can then identify plants exhibiting similarinterim outcomes and reallocate resources away from these plants (e.g.,less frequent monitoring, less frequent water and nutrient replenishing,relocation to a grow area with looser ambient controls, etc.) in orderto minimize resource load in the facility without substantive decrementin final plant outcomes.

The system can therefore leverage interim plant outcomes derived fromlow-, moderate-, and high resolution data collected within the facilityover high-, moderate-, and low-frequencies to inform intelligentallocation of resources to plants that may be influenced toward positiveoutcomes and intelligent early culling of plants unlikely to achievepositive outcomes.

Furthermore, the system can: implement methods and techniques describedabove to characterize relationships between: input parameters for plantsgrown in the facility up to a particular instance in the growth cycle ofthese plants; and interim outcomes of these plants at this instance inthe growth cycle. From these relationships, the system can generate amodified grow schedule from a particular interim outcome within a growthcycle to a particular positive final outcome; upon detecting a new plantat this particular interim outcome, the system can then autonomouslyimplement this modified grow schedule proximal this new plant in orderto drive this new plant toward this positive final outcome.

The system can similarly generate a modified grow schedule from a firstinterim outcome at a first time within a growth cycle (e.g., exhibitingweak correlation to a positive outcome) to a second interim outcome at alater time within the growth cycle (e.g., exhibiting strongercorrelation to a positive outcome); upon detecting a new plant at thefirst interim outcome at approximately the first instance within itsgrowth cycle, the system can then implement this modified grow scheduleproximal a module containing this new plant (e.g., by moving the moduleto a location in the grow area exhibiting ambient conditions betterapproximating conditions specified in this modified grow schedule, bymodifying a dosing schedule for reconditioning water in the module) inorder to drive this new plant toward the second interim outcome.

The system can implement the foregoing methods and techniques: tore-characterize relationships between input parameters and interim andfinal outcomes based on new data recorded by infrastructure in thefacility over time; and to revise the default and modified growschedules based on these revised relationships between input parametersand outcomes.

12. Testing Grow Schedules

Once the system thus generates a new (default or modified) growschedule, the system can test the grow schedule on a relatively smallbatch of plants. For example, over the course of a growth cycle of fixedduration (e.g., eight weeks), the system can implement closed-loopcontrols to automatically manipulate actuators within the facility toachieve target ranges of input parameters defined by the new growschedule for a test module containing a test batch of new plants basedon ambient, water quality, and plant-specific data collected by fixedand mobile infrastructure within the facility. In this example, thesystem can: automatically adjust brightness and/or times of day thatsupplemental lighting is active over a separate test grow area occupiedby the test module; adjust speed and/or times of day that fans near thetest module are active; adjust a humidity level setting ofhumidifier/dehumidifier near the test module; adjust a temperate settingof an HVAC system or a flow setting at an HVAC vent near the testmodule; adjust water nutrient targets and a nutrient refill schedule forthe test module; etc. over the course of the growth cycle for this batchof test plants.

In this foregoing example, throughout the growth cycle of these testplants, the system can implement methods and techniques described aboveto: collect images of plants in this test module; extract interim andfinal outcomes of these test plants from these images; and construct newvectors representing input parameters of these test plants throughouttheir growth cycles and their interim and final outcomes. The system canthen: automatically qualify the test plants as exhibiting positive orpoor interim and final outcomes based on proximity of these vectors toclusters associated with certain positive and poor outcomes; and/orprompt a human operator to label final outcomes of these plants aspositive or poor, as described above. The system can then confirmwhether the modified grow schedule yielded an expected outcome for thetest plants based on: differences between the modified grow schedule andactual exposure to input parameters for these test plants; and derivedcorrelations between input parameter time series and plant outcomesdescribed above.

The system can thus calculate a modified grow schedule based onhistorical input parameters and outcome data for plants grown at thefacility and test this modified grow schedule within a small set of testplants to confirm that this modified grow schedule achieves expectedresults before expanding this modified grow schedule throughout theentire facility. In particular, the system can: generate a new growschedule defining target ambient conditions for a new plant over agrowth cycle of the new plant and predicted to yield a positive finaloutcome for the new plant based on derived strengths of relationshipsbetween ambient conditions and final outcomes of plants grown in thefacility; autonomously adjust actuators within the facility based onambient and water quality data recorded by fixed and mobile sensors inthe facility in order to realize the new grow schedule proximal a moduleloaded with a new set of plants over a period of time; calculate apredicted final outcome of the new set of plants based on ambient andwater quality data recorded by the suite of fixed sensors during thisperiod of time and derived relationships between ambient conditions andwater quality and final outcomes of plants grown in the facility; andthen assign this new grow schedule to other modules throughout thefacility if final outcomes of these new plants align with the predictedpositive final outcome.

12.1 Select Input Parameter Testing and Select Outcome MetricAcquisition

The system can implement similar methods and techniques to: select aparticular input parameter for which a relationship with a particularoutcome metric is unknown or poorly supported; generate multiplemodified grow schedules with different magnitudes, tolerances, and/ortemporal variances of this particular input parameter; autonomouslyimplement these modified grow schedules across small sets of testplants; collect interim and final outcome metrics for these test plants;prompt a human operator to provide feedback regarding the particularoutcome metric for select plants in these test batches; and thenimplement regression techniques to recalculate the strength of therelationship between the particular input parameter and the particularoutcome metric based on ambient, water, and plant-specific datacollected throughout the growth cycles of these test plants.

In this implementation, the system can also selectively trigger thetransfer station to record ultra-high-resolution images of select testplants from this batch and/or selectively trigger tests plants from thisbatch for external composition, flavor, aroma, pest, and/or other testsin order to enable the system to access more complete descriptions ofthe final outcomes of these test plants.

In another implementation, in response to a final (or interim) outcomeof plants (e.g., colors, sizes, and/or geometries of these plants) in amodule differing from final (or interim) outcomes of a corpus of plantsgrown previously in the facility—as represented in the corpus of plantrecords—the system can: select a representative plant in this module;and prompt a human operator to indicate visual appeal of therepresentative plant in this module.

The system can update corresponding plant records with these new,selective data and recalculate relationships between input parameters,interim outcomes, and/or final outcomes, as described above. The systemcan then refine a default growth schedule and/or modified grow schedulesfor all or select plants in the facility—such as by modifying the targetmagnitude, tolerance limits, and/or temporal variance of this particularinput parameter throughout a plant's growth cycle—based on thisrelationship between the particular input parameter and the particularoutcome metric.

12.2 Input Parameter Tolerances

The system can implement similar methods and techniques to test loosertolerances for a particular input parameter, which may reduce resourceload for monitoring and maintaining plants growing in the facility. Forexample, the system can implement the foregoing methods and techniquesto define and implement a test grow schedule specifying loosenedtolerances for water nutrient levels for a batch of test plants, whichmay require the mover to execute water quality tests at a test moduleoccupied by the test plants at a reduced frequency, require the moverand/or the transfer station to replenish water and nutrients in the testmodule at lower frequency, and thus enable the mover and the transferstation to service more modules in a given period of time. The systemcan then: confirm whether greater variance in water quality in this testmodule over time yielded no or minimal decrease in final outcomes ofthese test plants based on images recorded by infrastructure in thefacility and/or supplied by a human operator; and update the defaultgrow schedule for plants across the facility to reflect these loosenedtolerances on water nutrient level accordingly.

Therefore, the system can isolate input parameters that exhibitedweakest relationships to positive outcomes in these plants and relaxtolerances or constraints on these input parameters in order to reduceproduction costs. The system can also: isolate other input parametersthat exhibited strongest relationships to positive outcomes; tightentolerances and constraints on these input parameters in order to betterensure positive outcomes in the future; and adjust sensing schedules forinfrastructure in the facility to collect data for these inputparameters at greater frequency in order to enable the system to rapidlyrespond to deviations from target values or from target ranges for theseinput parameters, thereby enabling the system to better ensure positiveoutcomes for all plants grown in the facility. The system can thusrefine the default grow schedule for future plants to reflect thesetested and verified target magnitudes, tolerance limits, and/or temporalvariances for these input parameters accordingly.

However, the system can implement any other methods or techniques: tofuse input parameter data collected through various infrastructure inthe facility and interim and final outcomes extracted from optical data,external test data, and human operator-supplied feedback for plantsgrown in the facility over time to calculate relationships between theseinput parameters and final outcomes; to recalculate a grow scheduleaccordingly; to test the new grow schedule; to propagate positive growschedule changes throughout the facility; and to repeat this processover time in order to maintain and increase yield of high-quality plantswhile reducing time and/or cost to produce each plant.

13. Local Closed-Loop Controls

The system can also track new plants during their growth cycles andimplement closed-loop controls to adjust grow schedules for these newplants based on differences and similarities between interim outcomes ofthese new plants and interim outcomes of previous plants of known finaloutcomes. For example, the system can: retrieve a first image of a newplant at a particular stage of development (e.g., at a particular timeafter seeding); access a set of template images of previous plants at ornear this same stage of development and of known final outcomes; andthen implement computer vision techniques to match the first image to anearest second image in the set of template images. If the known outcomeof the second plant is positive (e.g., near an “ideal” plant), thesystem can continue to implement the existing grow schedule for the newplant. However, if the known outcome of the second plant is negative orfar from an “ideal” final outcome, the system can: access a subset oftemplate images to include only images of previous plants at or nearthis same stage of development and of known positive final outcomes;implement computer vision techniques to match the first image to anearest third image in this subset of template images; and define acustom grow schedule for the new plant based on the third plant's inputparameter exposure after this stage of development. The system can thenautonomously implement the custom grow schedule at or around the firstplant in order to drive the first plant toward the known positiveoutcome of the third plant.

In another example, the system can: access a module-level image of a newmodule—containing a set of new plants and occupying the growarea—recorded by the mover at a particular time in the growth cycle ofthe set of new plants; and extract an interim outcome of a particularplant occupying the new module (e.g., a size, color, and or indicator ofpest presence) from this module-level image. The system can then scan acorpus of plant records of previous plants to identify a previous plantthat exhibited a previous interim outcome—at this same stage ofdevelopment—nearest the interim outcome of the particular plant (e.g.,based on similarities in interim outcomes at the same time sincereference dates for the particular and previous plants, as stored incorresponding plant records). If a final outcome of the previous plantdiffers from a target final outcome for plants grown in the facility(e.g., if the final outcome of the previous plant indicates a sizedifferent from a target plant size, a color differing from a targetplant color, and/or a degree of pest presence exceeding a threshold pestpresence), the system can flag the particular plant for culling from thenew module, such as immediately (e.g., prior to a scheduled completionof the growth cycle of the particular plant) or during a next scheduleddelivery of the new module to the transfer station. Thus upon deliveryof this new module to the transfer station, the robotic manipulator can:optically scan the new module; detect the particular plant in the newmodule; navigate an end effector to the particular plant; remove theparticular plant from the new module; and discard the particular plant,such as into a compost bin.

Alternatively, in the foregoing implementation, if the previousplant—matched to the particular plant at this particular developmentstage—is associated with a final outcome that sufficiently approximatesa target final outcome, the system can: extract ambient and waterquality conditions proximal the previous plant—from the particular stageof development to harvest—stored in a plant record associated with theprevious plant; shift target ambient and water quality conditions forthe new module into alignment with the ambient and water qualityconditions experienced by the previous plant after this developmentstage; and implement closed-loop controls to realize these modifiedtarget ambient and water quality conditions in and around the newmodule, thereby increasing likelihood that plants in the new moduleachieve positive final outcomes.

The system can implement similar methods and techniques for an entiregroup of plants in a module, across a group of modules arranged within aparticular region of the grow area in the facility, or across thefacility in its entirety. For example, if the system determines—fromlow-, moderate, or high-resolution images of the grow area, individualmodules, or individual plants—that plants within a module are generallyundersized for their current stage of development (e.g., are expressinginterim outcomes best matched to interim outcomes of previous plantsthat were undersized at a scheduled harvest time), the system can modifya grow schedule for this module to increase input parameters thatexhibit strong positive correlation to increased plant size (i.e., basedon derived relationships between input parameters and interim and finaloutcomes described above). In this example, the system can then:dispatch the mover to shift the module into a module location in thegrow area that receives a higher incidence of light (e.g., based on alight level map developed by the mover while navigating throughout thegrow area); and/or update a nutrient dose schedule for the module toreceive more nutrients and dispatch the mover to deliver the module to adosing station in the facility for adjustment of its water quality. Inanother example, if the system determines—from low-, moderate, orhigh-resolution images of the grow area, individual modules, orindividual plants—that plants within a module are too light in color,the system can modify a grow schedule for this module to decrease inputparameters that exhibit strong positive correlation to increased“yellowness” in plant foliage. In this example, the system can then:dispatch the mover to shift the module into a location in the grow areathat receives a lower incidence of light; and/or update a nutrient doseschedule for the module to slightly increase the pH of water anddispatch the mover to deliver the module to the dosing station foradjustment of its water quality.

However, the system can implement any other methods or techniques toadjust a grow schedule for a single plant or group of plants based ondifferences between optical data of these plants and optical data ofprevious plants—of known outcomes—at similar stages.

14. Humidity Control

In one variation, the system can: access a series of global and/or localhumidity data recorded by fixed and module sensors in the facility overa period of time; derive relationships between ambient humidity andfinal outcomes of plants grown in the facility; and set a targethumidity range in the grow area—predicted to yield positive finaloutcomes or otherwise not yield poor final outcomes—based on derivedrelationships between ambient humidity and final outcomes of plantsgrown in the facility. In particular, by collecting low-resolutionglobal humidity conditions at a high frequency (e.g., once per minute)through the suite of fixed sensors and higher-resolution local humidityconditions proximal known modules at a lower frequency through ahumidity sensor in the mover as ambient conditions in the facilitychange and as many batches of plants are grown and harvested in thefacility over time, the system can compile a dense corpus of humidityconditions experienced by plants throughout their growth cycles. Givenknown final outcomes of these plants, the system can derive arelationship between humidity conditions over various periods within thegrowth cycle of a plant and the final outcome of this plant.

Furthermore, humidity within the grow area may be a function of totalleaf area of plants occupying the grow area and may be represented in apredefined plant transpiration model or in a plant transpiration modeldeveloped (or “learned”) by the system over time. Therefore, the systemcan: estimate leaf areas of plants occupying modules in the grow areabased on features extracted from global, module-level, and/orplant-specific images recently recorded by sensors in the facility; andpredict a humidity in the grow area at a future time based on a sum ofleaf areas of plants occupying the grow area and the plant transpirationmodel. The system can then take preemptive action to control humidity inthe grow area if the predicted humidity in the grow area at the futuretime exceeds the target humidity range set by the system.

For example, to preemptively reduce humidity in the grow area, thesystem can: scan global, module-level, and/or plant-specific imagesrecently recorded by sensors in the facility for a group ofplants—occupying a particular module currently located in the growarea—exhibiting characteristics nearest a target outcome (i.e., aparticular module containing a group of plants most ready for harvest);dispatch the mover to autonomously deliver the particular module to thetransfer station; and then trigger the transfer station to harvest thegroup of plants from the particular module. Therefore, though theparticular group of plants may not have reached the conclusion of itsgrowth cycle, the system can automatically elect to harvest these plantsearly—given that the plans sufficiently approximate a target finaloutcome—in order to reduce total leaf area in the grow area, therebyreducing total transpiration in the grow area, and thus reducinghumidity in the grow area in the future without increasing powerconsumption by a dehumidifier or other HVAC controls in the facility.

In another example, the system can: scan global, module-level, and/orplant-specific images recently recorded by sensors in the facility for agroup of plants—occupying a particular module currently located in thegrow area—exhibiting interim outcomes that exhibit poor correlation witha target outcome (i.e., a particular module containing a group of plantsleast likely to reach a positive final outcome); dispatch the mover toautonomously deliver the particular module to the transfer station; andthen trigger the transfer station to cull (i.e., dispose of) the groupof plants from the particular module. Therefore, the system canselectively cull plants least likely to achieve positive final outcomesin order to reduce total leaf area in the grow area, reduce totaltranspiration in the grow area, and preemptively reduce future humidityin the grow area.

The system can implement similar methods and techniques to preemptivelyharvest and/or cull select plants occupying the grow area in order tomaintain future temperatures in the grow area—which may also be afunction of leaf area and plant transpiration—within a targettemperature range that the system has correlated with positive finalplant outcomes (or a target temperature range that the system has notcorrelated with poor final outcomes).

The systems and methods described herein can be embodied and/orimplemented at least in part as a machine configured to receive acomputer-readable medium storing computer-readable instructions. Theinstructions can be executed by computer-executable componentsintegrated with the application, applet, host, server, network, website,communication service, communication interface,hardware/firmware/software elements of a user computer or mobile device,wristband, smartphone, or any suitable combination thereof. Othersystems and methods of the embodiment can be embodied and/or implementedat least in part as a machine configured to receive a computer-readablemedium storing computer-readable instructions. The instructions can beexecuted by computer-executable components integrated bycomputer-executable components integrated with apparatuses and networksof the type described above. The computer-readable medium can be storedon any suitable computer readable media such as RAMs, ROMs, flashmemory, EEPROMs, optical devices (CD or DVD), hard drives, floppy drivesor any suitable device. The computer-executable component can be aprocessor but any suitable dedicated hardware device can (alternativelyor additionally) execute the instructions.

As a person skilled in the art will recognize from the previous detaileddescription and from the figures and claims, modifications and changescan be made to the embodiments of the invention without departing fromthe scope of this invention as defined in the following claims.

I claim:
 1. A method for monitoring growth of plants within a facilitycomprising: accessing a series of ambient conditions captured by a fixedsensor unit, arranged proximal a grow area within the facility, at afirst frequency during a first time period; writing the series ofambient conditions to a corpus of plant records associated with plantsoccupying a group of modules within the grow area based on knownlocations of modules within the grow area, the corpus of plant recordscomprising a first set of plant records associated with a first set ofplants occupying a first module, in the group of modules, during thefirst time period; dispatching a mover to autonomously deliver the firstmodule from the grow area to a transfer station; for each plant in thefirst set of plants: interpreting a final outcome of the plant based onplant-level data captured by the transfer station while transferring theplant out of the first module during a second time period succeeding thefirst time period; and writing the final outcome of the plant to a plantrecord, in the first set of plant records, associated with the plant;and deriving relationships between ambient conditions and final outcomesof plants within the facility based on the corpus of plant recordsassociated with plants grown in the facility.
 2. The method of claim 1:wherein aggregating global data captured by the fixed sensor unit duringthe first time period comprises aggregating ambient conditions measuredby the fixed sensor unit at the first frequency during the first timeperiod; wherein deriving relationships between ambient conditions andfinal outcomes based on the corpus of plant records comprises derivingstrengths of relationships between ambient conditions, measured at thefirst frequency, and outcomes of plants grown in the facility; andfurther comprising: generating a new grow schedule defining targetambient conditions for a new plant over a growth cycle and predicted toyield a target final outcome for the new plant based on strengths ofrelationships between ambient conditions and final outcomes of plantsgrown in the facility; and for a second module loaded with a second setof plants and occupying the grow area during a third time periodsucceeding the second time period, autonomously adjusting actuatorswithin the facility according to the new grow schedule based on ambientconditions measured by the fixed sensor unit during the third timeperiod.
 3. The method of claim 1, wherein interpreting the final outcomeof the plant for each plant in the first set of plants comprises, foreach plant in the first set of plants: accessing a plant-level image ofthe plant captured by the transfer station while transferring the plantout of the first module during the second time period; extracting a setof features from the plant-level image of the plant; and interpreting afinal outcome of the plant based on the set of features.
 4. The methodof claim 1: further comprising: dispatching the mover to autonomouslynavigate to the group of modules during the first time period; accessinga first set of module-level images of the first module captured by themover at a second frequency less than the first frequency during thefirst time period; interpreting intermediate outcomes of the first setof plants, occupying the first module during the first time period,based on the first set of module-level images; writing intermediateoutcomes of the first set of plants to the first set of plant records;and wherein deriving relationships between ambient conditions and finaloutcomes based on the corpus of plant records comprises derivingrelationships between ambient conditions, intermediate outcomes, andfinal outcomes based on the corpus of plant records.
 5. The method ofclaim 4, wherein writing intermediate outcomes of the first set ofplants to the first set of plant records comprises, for each plant inthe first set of plants: selecting a plant location in the first module;identifying a plant record associated with the plant location; andwriting an intermediate outcome of the plant, interpreted from regionsof the first set of module-level images corresponding to the plantlocation, to the plant record.
 6. The method of claim 1: furthercomprising: accessing a set of water quality data captured by the movervia a probe inserted into the first module by the mover during the firsttime period; and writing the set of water quality data to the first setof plant records; and wherein deriving relationships between ambientconditions and final outcomes based on the corpus of plant recordscomprises deriving relationships between ambient conditions, waterquality, and final outcomes based on the corpus of plant records.
 7. Themethod of claim 6, wherein accessing the set of water quality datacaptured by the mover during the first time period comprisesintermittently dispatching the mover to navigate to the grow area andsequentially sample water quality in modules in the group of modules atthe second frequency.
 8. The method of claim 1: wherein accessing theseries of ambient conditions comprises accessing a series of ambienthumidity data captured by the fixed sensor unit during the first timeperiod; wherein deriving relationships between ambient conditions andfinal outcomes based on the corpus of plant records comprises deriving arelationship between ambient humidity and final outcomes of plants grownin the facility; further comprising setting a target humidity range inthe grow area based on the relationship between ambient humidity andfinal outcomes of plants grown in the facility.
 9. The method of claim8, further comprising: estimating leaf areas of plants occupying modulesin the grow area; predicting a future humidity in the grow area at afuture time based on a combination of leaf areas of plants occupyingmodules in the grow area and a plant transpiration model; in response tothe future humidity in the grow area at the future time exceeding thetarget humidity range, identifying a second module, occupied by a secondset of plants nearing a target outcome, located in the grow area;dispatching the mover to autonomously deliver the second module to thetransfer station; and triggering the transfer station to harvest thesecond set of plants from the second module.
 10. The method of claim 1:wherein interpreting the final outcome of the plant for each plant inthe first set of plants comprises, for each plant in the first set ofplants: characterizing a final color, a final size, and a final geometryof the plant based on plant-level data of the plant captured by thetransfer station during the second time period; and calculating aviability score of the plant based on the final color, the final size,and the final geometry of the plant; and wherein writing the finaloutcome of the plant to the plant record for each plant in the first setof plants comprises, for each plant in the first set of plants, writingthe viability score of the plant to a plant record associated with theplant.
 11. A method for monitoring growth of plants within a facilitycomprising: during a first time period, dispatching a mover toautonomously navigate to a grow area within the facility; accessing afirst set of module-level data captured by the mover, at a firstfrequency, when proximal a first module occupying the grow area duringthe first period of time; interpreting intermediate outcomes of a firstset of plants occupying the first module during the first time periodbased on a first set of module-level data captured by the mover, at afirst frequency, when proximal the first module during the first periodof time; writing intermediate outcomes of the first set of plants to afirst set of plant records associated with the first set of plantsoccupying the first module during the first time period; during a secondtime period succeeding the first time period, dispatching the mover toautonomously deliver the first module from the grow area to a transferstation; for each plant in the first set of plants: interpreting a finaloutcome of the plant based on plant-level data captured by the transferstation while transferring the plant out of the first module during asecond time period succeeding the first time period; and writing thefinal outcome of the plant to a plant record, in the first set of plantrecords, associated with the plant; and deriving relationships betweenintermediate outcomes and final outcomes of plants within the facilitybased on the first set of plant records.
 12. The method of claim 11:further comprising: accessing a series of ambient conditions captured bya fixed sensor unit, arranged proximal the grow area within thefacility, at a second frequency greater than the first frequency duringa third time period preceding the second time period; writing the seriesof ambient conditions to a corpus of plant records associated withplants occupying a group of modules within the grow area based on knownlocations of modules within the grow area during the third time period,the corpus of plant records comprising the first set of plant records;wherein deriving relationships between intermediate outcomes and finaloutcomes from the first set of plant records comprises derivingrelationships between ambient conditions, intermediate outcomes, andfinal outcomes based on the corpus of plant records.
 13. The method ofclaim 11: wherein accessing the first set of module-level data capturedby the mover comprises accessing a module-level image of the firstmodule captured by the mover when proximal the first module during thefirst time period; wherein interpreting intermediate outcomes of thefirst set of plants occupying the first module during the first timeperiod based on the first set of module-level data comprises, for eachplant in the first set of plants occupying the first module during thefirst time period: identifying a first region of the module-level imagedepicting the plant; extracting a first set of features from the firstregion of the module-level image; and interpreting an intermediateoutcome of the plant based on the first set of features; and whereininterpreting the final outcome of the plant for each plant in the firstset of plants comprises, for each plant in the first set of plants:accessing a plant-level image of the plant captured by the transferstation while transferring the plant out of the first module during thesecond time period; extracting a second set of features from theplant-level image of the plant; and interpreting a final outcome of theplant based on the second set of features.
 14. A method for monitoringgrowth of plants within a facility comprising: aggregating global datacaptured by a fixed sensor unit, arranged proximal a grow area withinthe facility, at a first frequency during a grow period; aggregatinginterim conditions of a first set of plants, occupying a first module inthe grow area, based on module-level data captured by a mover at asecond frequency less than the first frequency while proximal the firstmodule during the period of time; dispatching the mover to autonomouslydeliver the first module to a transfer station; interpreting outcomes ofthe first set of plants based on plant-level data captured by thetransfer station while transferring plants out of the first modulefollowing conclusion of the grow period; and deriving relationshipsbetween global data, interim conditions, and outcomes from a corpus ofplant records associated with plants grown in the facility.
 15. Themethod of claim 14: wherein aggregating global data captured by thefixed sensor unit during the grow period comprises aggregating ambientconditions measured by the fixed sensor unit at the first frequencyduring the grow period; wherein deriving relationships between globaldata, interim conditions, and outcomes based on the corpus of plantrecords comprises deriving strengths of relationships between ambientconditions, measured at the first frequency, and outcomes of plantsgrown in the facility; and further comprising: generating a new growschedule defining target ambient conditions for a new plant over agrowth cycle and predicted to yield a target final outcome for the newplant based on strengths of relationships between ambient conditions andfinal outcomes of plants grown in the facility; and for a second moduleloaded with a second set of plants and occupying the grow area during asecond grow period succeeding the grow period, autonomously adjustingactuators within the facility according to the new grow schedule basedon ambient conditions measured by the fixed sensor unit during thesecond grow period.
 16. The method of claim 14: wherein aggregatingglobal data captured by the fixed sensor unit during the grow periodcomprises: accessing a global image of a group of modules, comprisingthe first module, captured by the fixed sensor unit at a first timeduring the grow period; identifying a first region of the global imagedepicting the first module; extracting a first set of features from thefirst region of the global image; interpreting a global condition of thefirst set of plants at the first time based on the first set offeatures; and writing the global condition to a first set of plantrecords, in the corpus of plant records, associated with the first setof plants; and wherein deriving relationships between global data,interim conditions, and outcomes based on the corpus of plant recordscomprises deriving relationships between the global condition, interimconditions, and outcomes based on the first set of plant records. 17.The method of claim 14, wherein aggregating interim conditions of thefirst set of plants comprises: accessing a module-level image of thefirst module captured by the mover when proximal the first module at asecond time succeeding the first time; for each plant in the first setof plants occupying the first module at the second time: identifying asecond region of the module-level image depicting the plant; extractinga second set of features from the second region of the module-levelimage; interpreting an intermediate outcome of the plant based on thesecond set of features; and writing the intermediate outcome to a plantrecord, in the first set of plant records, associated with the plant;wherein interpreting outcomes of the first set of plants based onplant-level data captured by the transfer station while transferringplants out of the first module following conclusion of the grow periodcomprises interpreting final outcomes of the first set of plants basedon plant-level data captured by the transfer station while transferringplants out of the first module following conclusion of the grow period;and wherein deriving relationships between global data, intermediateoutcomes, conditions, and final outcomes based on the corpus of plantrecords comprises deriving relationships between the global data,interim conditions, and final outcomes based on the first set of plantrecords.
 18. The method of claim 14: wherein interpreting outcomes ofthe first set of plants based on plant-level data captured by thetransfer station comprises, for each plant in the first set of plants:accessing a plant-level image of the plant captured by the transferstation while transferring the plant out of the first module during thesecond time period succeeding the grow period; extracting a third set offeatures from the plant-level image of the plant; interpreting a finaloutcome of the plant based on the third set of features; and writing thefinal outcome to a plant record, in the first set of plant records,associated with the plant; and wherein deriving relationships betweenglobal data, interim conditions, and outcomes based on the corpus ofplant records comprises deriving relationships between the global data,interim conditions, and final outcomes based on the first set of plantrecords.
 19. The method of claim 14: wherein aggregating global datacaptured by the fixed sensor unit during the grow period comprises:accessing a set of ambient air quality data captured by the fixed sensorunit during the grow period; and writing the set of ambient air qualitydata to a first set of plant records associated with the first set ofplants occupying the first module located in the grow area during thegrow period; wherein aggregating interim conditions of the first set ofplants during the period of time comprises: accessing a set waterquality data captured by the mover via a probe inserted into the firstmodule by the mover during the first time period; and writing the set ofwater quality data to the first set of plant records; and whereinderiving relationships between global data, interim conditions, andoutcomes based on the corpus of plant records comprises derivingrelationships between ambient air quality, water quality, and finaloutcomes based on the corpus of plant records comprising the first setof plant records.
 20. The method of claim 14: wherein interpretingoutcomes of the first set of plants based on plant-level data capturedby the transfer station while transferring plants out of the firstmodule following conclusion of the grow period comprises intermediateoutcomes of the first set of plants based on plant-level data capturedby the transfer station while transferring plants out of the firstmodule following conclusion of the grow period; and further comprising:dispatching the mover to autonomously deliver a second module to thegrow area, the second module occupied by a subset of the first set ofplants transferred out of the first module by the transfer stationfollowing conclusion of the grow period; aggregating global datacaptured by the fixed sensor unit during a finishing period followingconclusion of the grow period; dispatching the mover to autonomouslydeliver the second module to the transfer station; and interpretingfinal outcomes of the subset of the first set of plants based onplant-level data captured by the transfer station while transferringplants out of the second module following conclusion of the finishingperiod; and wherein deriving relationships between global data, interimconditions, and outcomes based on the corpus of plant records comprisesderiving relationships between global data captured during the growperiod, global data captured during the finishing period, intermediateoutcomes, and final outcomes based on the corpus of plant records ofplants grown in the facility.